Figure 5 code
Frances Shepherd
2023-11-14
#if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
#remotes::install_version("Seurat", version = "4.3.0", lib = "~/bin/seurat_4.3.0")
#install.packages("ggplot2")
#install.packages("dplyr")
#BiocManager::install("glmGamPoi")
#install.packages("sctransform")
#install.packages("purrr")
#install.packages("HGNChelper")
#install.packages("openxlsx")
library(Seurat, lib.loc = "~/bin/seurat_4.3.0")## Attaching SeuratObject## 'SeuratObject' was built with package 'Matrix' 1.6.3 but the current
## version is 1.6.5; it is recomended that you reinstall 'SeuratObject' as
## the ABI for 'Matrix' may have changed## Seurat v4 was just loaded with SeuratObject v5; disabling v5 assays and
## validation routines, and ensuring assays work in strict v3/v4
## compatibility modelibrary(sctransform)
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(HGNChelper)
library(openxlsx)
library(stringr)
library(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, lastlibrary(purrr)##
## Attaching package: 'purrr'## The following object is masked from 'package:data.table':
##
## transposelibrary(forcats)#Load control and Cal09 infected gene expression matrices (output by CellRanger)
mock.data <- Read10X(data.dir = "")
cal.data <- Read10X(data.dir = "")mock <- CreateSeuratObject(counts = mock.data,
min.features = 200,
project = "mock")## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')cal <- CreateSeuratObject(counts = cal.data,
min.features = 200,
project = "cal")## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')#Add a column which describes the experimental condition for each dataset
mock[["condition"]] <- "mock"
cal[["condition"]] <- "cal"
#Merge two Seurat objects
merged_seurat <- merge(x = mock,
y = cal,
add.cell.id = c("mock", "cal"))
#Calculate mitochondrial genes
merged_seurat[["percent.mt"]] <- PercentageFeatureSet(merged_seurat, pattern = "^MT-")
#Calculate number of genes detected per UMI (give a sense of data complexity)
merged_seurat[["log10GenesPerUMI"]] <- log10(merged_seurat$nFeature_RNA) / log10(merged_seurat$nCount_RNA)
#Create column that replicates the rownames
merged_seurat[["cells"]] <- rownames(merged_seurat@meta.data)QC
Cells captured per condition:
#pdf("final/results/Cells per condition bar graph.pdf")
merged_seurat@meta.data %>%
ggplot(aes(x=condition, fill=condition)) +
geom_bar() +
theme_classic() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
theme(plot.title = element_text(hjust=0.5, face="bold")) +
ggtitle("Number of cells per condition")
#dev.off()UMI counts per cell:
#pdf("final/results/UMI counts per cell density plot.pdf")
merged_seurat@meta.data %>%
ggplot(aes(color=condition, x=nCount_RNA, fill= condition)) +
geom_density(alpha = 0.2) +
scale_x_log10() +
theme_classic() +
ylab("Cell density") +
geom_vline(xintercept = 500, linetype = "dotted") +
geom_vline(xintercept = 100000, linetype = "dotted") +
ggtitle("Number of UMIs per cell")
#dev.off()Number of genes per cell:
#pdf("final/results/Genes per cell density plot.pdf")
merged_seurat@meta.data %>%
ggplot(aes(color=condition, x=nFeature_RNA, fill= condition)) +
geom_density(alpha = 0.2) +
theme_classic() +
scale_x_log10() +
ggtitle("Genes detected per cell") +
geom_vline(xintercept = 600, linetype = "dotted")
#dev.off()Bimodal distribution is typical of NHBE cells.
Number of genes per UMI:
#pdf("final/results/Genes per UMI density plot.pdf")
merged_seurat@meta.data %>%
ggplot(aes(color=condition, x=log10GenesPerUMI, fill= condition)) +
geom_density(alpha = 0.2) +
theme_classic() +
scale_x_log10() +
geom_vline(xintercept = 0.8, linetype = "dotted") +
ggtitle("Average genes per UMI\n(Complexity metric)")
#dev.off()Histogram of the mitochondrial gene counts:
#pdf("final/results/MT percentages density plot.pdf")
merged_seurat@meta.data %>%
ggplot(aes(color=condition, x=percent.mt, fill=condition)) +
geom_density(alpha = 0.2) +
scale_x_log10() +
theme_classic() +
geom_vline(xintercept = 40, linetype = "dotted") +
ggtitle("Percent mitochondrial gene counts")
#dev.off()Higher mitochondrial gene percentages is also typical of NHBE cells. Assess cutoffs of UMIs between 500 and 100,000 and number of unique features/transcripts at 700:
#pdf("final/results/Combined QC metrics plot.pdf")
merged_seurat@meta.data %>%
ggplot(aes(x=nCount_RNA, y=nFeature_RNA, color=percent.mt)) +
geom_point() +
scale_colour_gradient(low = "gray90", high = "black") +
#scale_colour_gradientn(colours = "white", "white", "gray", "black",
# breaks = c(0, ))
stat_smooth(method=lm) +
scale_x_log10() +
scale_y_log10() +
theme_classic() +
geom_vline(xintercept = 500, linetype = "dotted") +
geom_vline(xintercept = 100000, linetype = "dotted") +
geom_hline(yintercept = 600, linetype = "dotted") +
facet_wrap(~condition)## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation: colour
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## The following aesthetics were dropped during statistical transformation: colour
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
#dev.off()After applying above filters, plus a filter for MT% greater than 40%. Look at violin plots of the main QC metrics after filtering:
merged_seurat <- subset(merged_seurat,
subset = (nCount_RNA > 500) &
(nCount_RNA < 100000) &
(nFeature_RNA > 600) &
(log10GenesPerUMI > 0.8) &
(percent.mt < 40))
#pdf("final/results/QC violin plots after filtering.pdf")
VlnPlot(merged_seurat, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, group.by = "condition")
#dev.off()Check for cell cycle gene expression
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
merged_seurat <- NormalizeData(merged_seurat) #Normalize the data
merged_seurat <- FindVariableFeatures(merged_seurat, selection.method = "vst") #Find variable genes
merged_seurat <- ScaleData(merged_seurat) #Scale the data## Centering and scaling data matrixmerged_seurat <- RunPCA(merged_seurat) # Perform PCA on the variable genes## PC_ 1
## Positive: S100A2, S100A9, KRT5, KRT17, KRT6A, SERPINB3, LY6D, FABP5, TIMP1, KRT13
## SPINK5, SFN, FRMD6, CCND1, DSG3, AGR2, S100A8, IGFBP6, PHLDB2, TUBB
## KRT15, MMP28, TP63, FLNA, IL1RN, KRT14, TNC, COL17A1, LGALS7, RBP1
## Negative: AGBL4, DNAH11, DCDC1, CFAP54, DNAH3, WDR49, SERPINI2, HYDIN, VWA3A, CFAP299
## CDHR3, DNAH9, DNAH7, LMNTD1, RP1, DNAH12, DNAH6, PACRG, DNAI1, ADGB
## FAM227A, CFAP43, SPEF2, DNAAF1, CFAP61, ARMC3, ZBBX, CFAP46, STK33, TTC29
## PC_ 2
## Positive: KRT5, S100A2, COL17A1, FLNA, IGFBP6, SFN, BCAM, LAMA3, TP63, S100A10
## TENM2, DST, CCDC3, FRMD6, KRT17, C16orf74, CAV1, IGFBP7, ANKH, KRT15
## RBBP8, KRT6A, TUBB6, CALD1, FHOD3, TIMP1, CDH13, MMP28, COL4A6, KRT14
## Negative: CD55, WFDC2, LCN2, BPIFB1, C3, CP, MUC16, PIGR, SLPI, TMC5
## SLC6A14, CXCL17, ABCA13, FAM3D, PLEKHS1, MUC1, ANKRD36C, RIMS1, PPP1R9A, CATSPERB
## FER1L6, FCGBP, MSMB, CAPN13, ARHGEF38, MUC5B, CEACAM6, MUC20, MACC1, S100P
## PC_ 3
## Positive: PARD3B, FTO, RARB, SORBS2, ITGB8, LAMC2, STK38L, FMNL2, GPRC5A, RNF213
## HDAC9, ANTXR1, RIMS2, ITPKC, DST, PTK2B, INPP4B, HMGA2, CD55, TNS3
## CATSPERB, WDPCP, TTC6, KLF7, NEDD9, PLEKHG1, ERBB4, ATP10B, NBEA, B4GALNT3
## Negative: FTH1, DYNLL1, CSTB, IFI27, TUBB4B, S100A9, CAPS, MORN2, DYNLT1, C20orf85
## SCGB1A1, SCGB3A1, SLPI, SERPINB3, LCN2, C9orf24, TUBA1A, CRIP1, PIERCE1, TMEM190
## HSP90AA1, WFDC2, PIFO, TPPP3, SAA1, UFC1, CFAP276, CAPSL, OMG, CETN2
## PC_ 4
## Positive: TOP2A, DIAPH3, MKI67, CENPF, ASPM, RRM2, MELK, ANLN, TPX2, KNL1
## NCAPG, KIF14, KIF18B, GTSE1, PBK, ENSG00000284906, CEP55, CENPE, NCAPH, CDK1
## SPC25, KIF15, CKAP2L, BRCA2, ESCO2, KIF4A, BIRC5, TYMS, NDC80, TACC3
## Negative: IGFBP7, DST, COL17A1, CCND2, TNS3, BCAM, IGFBP3, PTPRQ, PALMD, LOXL4
## RBBP8, SOX6, TENM2, MMP13, ANKH, COL12A1, FYB1, FHOD3, LAMA3, PHLDA1
## COL4A6, PMEPA1, SLC7A2, TINAGL1, ABCC3, CDH13, CLU, ADGRL3, TNC, CCDC3
## PC_ 5
## Positive: SERPINB3, KRT13, KRT4, LY6D, SNX31, SERPINB1, SERPINB13, LYPD3, CLCA4, SPINK5
## PLCE1, PLAUR, RNF168, DSG3, GSTA1, LBH, AQP5, MDM2, ABTB3, GPX2
## CADM1, NR4A3, POU2AF1, SMIM35, SCEL, CCDC60, STRBP, ITPKC, IGSF11, RERG
## Negative: SAA1, IFIT3, SAA2, IFIT1, IFI6, ISG15, IDO1, IFITM1, HLA-DRB1, SLC26A4
## MX2, DDX60L, MX1, MT2A, EPSTI1, IFIT2, HERC6, HLA-DPA1, RSAD2, SAMD9L
## HLA-DPB1, DDX60, USP18, CXCL6, HERC5, SLC5A8, RNF213, HLA-DQA1, HLA-DRA, CXCL5merged_seurat <- CellCycleScoring(merged_seurat,
g2m.features = g2m.genes,
s.features = s.genes)## Warning: The following features are not present in the object: MLF1IP, not
## searching for symbol synonyms## Warning: The following features are not present in the object: FAM64A, HN1, not
## searching for symbol synonymsPCA colored by cell cycle phase:
#pdf("final/results/Cell cycle PCA plot.pdf")
DimPlot(merged_seurat,
reduction = "pca",
group.by= "Phase")
#dev.off()The left side of the graph is dominated by G1 and G2M cells. I will regress out variation due to cell cycle genes in the next step.
Check for MT gene expression
Do the same PCA for mitochondrial gene expression
#Get distribution of MT gene expression:
summary(merged_seurat@meta.data$percent.mt)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1665 8.4534 14.0768 14.0511 18.8004 39.9950#Based on these values, create a new variable to bin cells based on level of MT gene expression
merged_seurat@meta.data$mitoFr <- cut(merged_seurat@meta.data$percent.mt,
breaks=c(-Inf, 8.4534, 14.0768, 18.8004, Inf),
labels=c("Low","Medium","Medium high", "High"))Plot PCA and color according to their MT gene expression “bin”.
#pdf("final/results/MT fraction PCA plot.pdf")
DimPlot(merged_seurat,
size=10,
reduction = "pca",
group.by = "mitoFr")
#dev.off()There are differences on the PC_1, especially in low MT-expressing cells, so I will regress this variation out as well.
Normalization with scTransform
#Split the object into mock and cal conditions
split_seurat <- SplitObject(merged_seurat, split.by = "condition")
#Perform SCtransformation for the mock dataset first
mock <- SCTransform(split_seurat[["mock"]],
vst.flavor = "v2",
vars.to.regress = c("percent.mt", "S.Score", "G2M.Score"))## vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.## Calculating cell attributes from input UMI matrix: log_umi## Total Step 1 genes: 16548## Total overdispersed genes: 15496## Excluding 1052 genes from Step 1 because they are not overdispersed.## Variance stabilizing transformation of count matrix of size 17313 by 12610## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells##
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|======================================================================| 100%## Setting estimate of 173 genes to inf as theta_mm/theta_mle < 1e-3## # of step1 poisson genes (variance < mean): 0## # of low mean genes (mean < 0.001): 826## Total # of Step1 poisson genes (theta=Inf; variance < mean): 182## Total # of poisson genes (theta=Inf; variance < mean): 1788## Calling offset model for all 1788 poisson genes## Found 197 outliers - those will be ignored in fitting/regularization step## Ignoring theta inf genes## Replacing fit params for 1788 poisson genes by theta=Inf## Setting min_variance based on median UMI: 0.04## Second step: Get residuals using fitted parameters for 17313 genes##
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|======================================================================| 100%## Computing corrected count matrix for 17313 genes##
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|======================================================================| 100%## Calculating gene attributes## Wall clock passed: Time difference of 1.327199 mins## Determine variable features## Place corrected count matrix in counts slot## Regressing out percent.mt, S.Score, G2M.Score## Centering data matrix## Set default assay to SCT#Then for the cal-infected dataset
cal <- SCTransform(split_seurat[["cal"]],
vst.flavor = "v2",
vars.to.regress = c("percent.mt", "S.Score", "G2M.Score"))## vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.## Calculating cell attributes from input UMI matrix: log_umi## Total Step 1 genes: 17139## Total overdispersed genes: 16338## Excluding 801 genes from Step 1 because they are not overdispersed.## Variance stabilizing transformation of count matrix of size 18033 by 17955## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells##
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|======================================================================| 100%## Setting estimate of 197 genes to inf as theta_mm/theta_mle < 1e-3## # of step1 poisson genes (variance < mean): 0## # of low mean genes (mean < 0.001): 897## Total # of Step1 poisson genes (theta=Inf; variance < mean): 209## Total # of poisson genes (theta=Inf; variance < mean): 1645## Calling offset model for all 1645 poisson genes## Found 211 outliers - those will be ignored in fitting/regularization step## Ignoring theta inf genes## Replacing fit params for 1645 poisson genes by theta=Inf## Setting min_variance based on median UMI: 0.16## Second step: Get residuals using fitted parameters for 18033 genes##
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|======================================================================| 100%## Calculating gene attributes## Wall clock passed: Time difference of 1.949843 mins## Determine variable features## Place corrected count matrix in counts slot## Regressing out percent.mt, S.Score, G2M.Score## Centering data matrix## Set default assay to SCTIntegration
rm(split_seurat)
mock_cal <- list(mock = mock, cal = cal)
integ_features <- SelectIntegrationFeatures(object.list = mock_cal,
nfeatures = 3000)
rm(mock)
rm(cal)
# Prepare the SCT list object for integration
mock_cal <- PrepSCTIntegration(object.list = mock_cal,
anchor.features = integ_features)
#Finally perform the actual integration (uses what's called canonical correlation analysis)
integ_anchors <- FindIntegrationAnchors(object.list = mock_cal,
normalization.method = "SCT",
anchor.features = integ_features)## Finding all pairwise anchors## Running CCA## Merging objects## Finding neighborhoods## Finding anchors## Found 32456 anchors## Filtering anchors## Retained 24496 anchorsrm(mock_cal)
seurat_integrated <- IntegrateData(anchorset = integ_anchors,
normalization.method = "SCT")## Merging dataset 1 into 2## Extracting anchors for merged samples## Finding integration vectors## Finding integration vector weights## Integrating data#Perform PCA
seurat_integrated <- RunPCA(object = seurat_integrated)## PC_ 1
## Positive: S100A2, KRT5, S100A9, IGFBP6, KRT17, KRT6A, KRT15, KRT13, SERPINB3, LY6D
## RPS18, RPS12, KRT19, TPT1, RPLP1, KRT14, S100A8, RPS2, FABP5, RPL10
## S100A14, TIMP1, EEF1A1, RPL13, CSTA, SFN, HSPB1, TMSB10, PHLDB2, FGFBP1
## Negative: CFAP299, DNAH12, DNAH7, WDR49, AGBL4, CFAP54, HYDIN, LRRIQ1, DNAH6, DNAH5
## DCDC1, DNAH3, SPAG17, TMEM232, ARMC3, PACRG, DNAH11, CFAP47, DNAH9, NEK10
## CDHR3, RFX3, SPATA17, RP1, CFAP43, SERPINI2, ZBBX, ULK4, STK33, FANK1
## PC_ 2
## Positive: S100A2, IGFBP6, KRT14, KRT15, KRT5, LAMA3, KRT17, IGFBP7, DST, KRT6A
## COL17A1, S100A10, SFN, TIMP1, MT2A, LGALS1, TENM2, CCDC3, CAV1, PHLDB2
## BCAM, TP63, FABP5, RBBP8, FLNA, KRT13, CDH13, FRMD6, NRG1, TINAGL1
## Negative: ANKRD36C, BPIFB1, CD55, PIGR, FCGBP, RIMS1, MUC5B, LCN2, RUNX1, ABCA13
## CP, PLAC8, WFDC2, MACC1, CAPN13, CEACAM6, TMC5, SLC6A14, MUC16, C3
## FER1L6, SLPI, MSMB, PLEKHS1, RARRES1, MUC4, SCGB3A1, GPRC5A, HDAC9, NEBL
## PC_ 3
## Positive: SERPINB3, S100A9, LCN2, LY6D, SLPI, WFDC2, GSTA1, SAA2, SERPINB1, TSPAN1
## CAPS, SCGB3A1, KRT4, SMIM22, MORN2, SERPINB4, AQP5, PI3, CYP4B1, CRIP1
## C9orf24, KRT13, C20orf85, S100A6, CSTA, PIERCE1, UPK1B, PIFO, PSCA, TUBA1A
## Negative: DST, IGFBP6, LAMA3, KRT14, ANKRD36C, COL17A1, KRT15, TENM2, RBBP8, CCDC3
## ITGB1, LGALS1, CAV1, LAMC2, GRIP1, CDH13, TNC, BCAM, TNS3, NRG1
## CBLB, FLNA, KRT17, LAMB3, TP63, ANKH, S100A2, MT2A, TINAGL1, FHOD3
## PC_ 4
## Positive: KRT13, LY6D, KRT4, SERPINB3, SERPINB1, CHST9, DENND2C, CSTA, SPINK5, SERPINB13
## MAPK10, PLAUR, LBH, DSG3, RNF168, AQP3, GSTA1, MYO1E, MTSS1, DSP
## S100A9, CLCA4, PLCE1, MAP3K8, ITPKC, RAB11FIP1, LYPD3, MDM2, ZNF750, SERPINB4
## Negative: SAA2, SAA1, BPIFB1, PIGR, LCN2, FCGBP, RARRES1, SLC26A4, IDO1, MSMB
## CEACAM6, MUC5B, CXCL6, C3, WFDC2, DEFB4A, MT2A, HLA-DRB1, IGFBP6, SLPI
## PROM1, KRT14, IGFBP7, LYPD2, SCGB3A1, HLA-DRA, HLA-DPA1, HLA-DPB1, DST, CXCL5
## PC_ 5
## Positive: OMG, TMEM190, IGFBP7, C9orf24, RRAD, CRIP1, MORN2, GPRC5A, CFAP276, ODF3B
## C20orf85, FAM216B, PIERCE1, MACC1, RIIAD1, PLAC8, MKI67, CETN2, SNTN, WDR38
## NWD1, PIFO, PLAAT2, ANKFN1, PLAUR, TUBA1A, C11orf97, CAPSL, CTXN1, MUC16
## Negative: CDC20B, BPIFB1, CCNO, SCGB3A1, FOXN4, CEP128, SMIM35, ANLN, SCGB1A1, SLPI
## LCN2, PLK4, E2F7, CCDC171, DEUP1, MSMB, CEP112, PLCE1, KIF24, PCM1
## SLC4A8, WFDC2, CROCC2, STRBP, STIL, HES6, SDK1, H2BC11, PIGR, PI3# Run UMAP
seurat_integrated <- RunUMAP(seurat_integrated,
dims = 1:40,
reduction = "pca")## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 13:12:37 UMAP embedding parameters a = 0.9922 b = 1.112## Found more than one class "dist" in cache; using the first, from namespace 'spam'## Also defined by 'BiocGenerics'## 13:12:37 Read 30565 rows and found 40 numeric columns## 13:12:37 Using Annoy for neighbor search, n_neighbors = 30## Found more than one class "dist" in cache; using the first, from namespace 'spam'## Also defined by 'BiocGenerics'## 13:12:37 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 13:12:42 Writing NN index file to temp file /tmp/RtmpoN4gwM/file24b7b02634ac52
## 13:12:42 Searching Annoy index using 1 thread, search_k = 3000
## 13:12:55 Annoy recall = 100%
## 13:12:57 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 13:12:59 Initializing from normalized Laplacian + noise (using irlba)
## 13:13:01 Commencing optimization for 200 epochs, with 1358062 positive edges
## 13:13:36 Optimization finished# Plot UMAP
#pdf("final/results/Integrated UMAP-by condition.pdf")
DimPlot(seurat_integrated, group.by = "condition") 
#dev.off()Clustering cells
seurat_integrated <- FindNeighbors(object = seurat_integrated,
dims = 1:40)## Computing nearest neighbor graph## Computing SNNseurat_integrated <- FindClusters(object = seurat_integrated,
resolution = 0.2)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 30565
## Number of edges: 1162079
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9481
## Number of communities: 12
## Elapsed time: 5 seconds#Change factor levels of seurat integrated conditions:
seurat_integrated@meta.data$condition <- factor(seurat_integrated@meta.data$condition, levels = c("mock", "cal"))
#pdf("final/results/Integrated UMAP-by cluster.pdf")
DimPlot(seurat_integrated,
reduction = "umap",
label = TRUE,
label.size = 6,
split.by = "condition") &
ggtitle("UMAP of integrated data-by cluster") &
labs(caption = "Resolution = 0.2")
#dev.off()Graphing percent flu infection
#Reset default assay
DefaultAssay(seurat_integrated) <- "RNA"
#Calculate percent flu genes
seurat_integrated[["percent.flu"]] <- PercentageFeatureSet(seurat_integrated, pattern = "Cal-")
summary(seurat_integrated@meta.data$percent.flu)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.01811 0.10368 0.04241 43.25438Make violin plot of percent flu read distribution, showing all percentages:
#pdf("final/results/Percent flu per condition.pdf")
VlnPlot(seurat_integrated, features = c("percent.flu"), group.by = "condition")
#dev.off()There are some high percentages that make the violin plot hard to read. Also made a violin plot of percent flu for everything below 1 percent:
#pdf("final/results/Percent flu per condition-below 1 percent.pdf")
VlnPlot(subset(seurat_integrated,
subset = percent.flu < 1), features = c("percent.flu"), group.by = "condition") &
ggtitle("percent.flu for only cells with < 1% flu") &
geom_hline(yintercept = 0.07, linetype = "dotted")
#dev.off()#Create bins for low, medium, or highly flu-infected cells.
seurat_integrated@meta.data$flu_fraction <- cut(seurat_integrated@meta.data$percent.flu,
breaks=c(-Inf, 0.07, 1, 10, Inf),
labels=c("Uninfected","Low","Medium", "High"))
mycols <- c("lightgray", "#FFFF99", "orange", "red")
#pdf("final/results/Fraction flu reads per cell-UMAP.pdf)
DimPlot(seurat_integrated,
size=10,
reduction = "umap",
group.by = "flu_fraction",
split.by = "condition",
cols = alpha(mycols,0.66)) &
ggtitle("Fraction of flu reads per cell\nCombined analysis") &
labs(caption = "Uninfected: < 0.07% flu\nLow: 0.07-1% flu\nMedium: 1-10% flu\nHigh: >10% flu")
#dev.off()Identifying cell types
Find conserved markers in each cluster:
# Create function to get conserved markers for any given cluster. This works similarly to FindAllMarkers but uses the correct analysis with the integrated dataset.
get_conserved <- function(cluster){
FindConservedMarkers(seurat_integrated,
assay = "RNA",
ident.1 = cluster,
grouping.var = "condition",
only.pos = TRUE) %>%
tibble::rownames_to_column(var = "gene") %>%
cbind(cluster_id = cluster, .)
}
#Get markers for all 12 clusters in the integrated dataset
conserved_markers <- map_dfr(c(0:11), get_conserved)## Testing group mock: (0) vs (3, 1, 2, 6, 4, 7, 5, 10, 11, 8, 9)## Testing group cal: (0) vs (4, 5, 1, 2, 3, 8, 6, 9, 11, 7, 10)## Testing group mock: (1) vs (0, 3, 2, 6, 4, 7, 5, 10, 11, 8, 9)## Testing group cal: (1) vs (4, 5, 0, 2, 3, 8, 6, 9, 11, 7, 10)## Testing group mock: (2) vs (0, 3, 1, 6, 4, 7, 5, 10, 11, 8, 9)## Testing group cal: (2) vs (4, 5, 1, 0, 3, 8, 6, 9, 11, 7, 10)## Testing group mock: (3) vs (0, 1, 2, 6, 4, 7, 5, 10, 11, 8, 9)## Testing group cal: (3) vs (4, 5, 1, 0, 2, 8, 6, 9, 11, 7, 10)## Testing group mock: (4) vs (0, 3, 1, 2, 6, 7, 5, 10, 11, 8, 9)## Testing group cal: (4) vs (5, 1, 0, 2, 3, 8, 6, 9, 11, 7, 10)## Testing group mock: (5) vs (0, 3, 1, 2, 6, 4, 7, 10, 11, 8, 9)## Testing group cal: (5) vs (4, 1, 0, 2, 3, 8, 6, 9, 11, 7, 10)## Testing group mock: (6) vs (0, 3, 1, 2, 4, 7, 5, 10, 11, 8, 9)## Testing group cal: (6) vs (4, 5, 1, 0, 2, 3, 8, 9, 11, 7, 10)## Testing group mock: (7) vs (0, 3, 1, 2, 6, 4, 5, 10, 11, 8, 9)## Testing group cal: (7) vs (4, 5, 1, 0, 2, 3, 8, 6, 9, 11, 10)## Testing group mock: (8) vs (0, 3, 1, 2, 6, 4, 7, 5, 10, 11, 9)## Testing group cal: (8) vs (4, 5, 1, 0, 2, 3, 6, 9, 11, 7, 10)## Testing group mock: (9) vs (0, 3, 1, 2, 6, 4, 7, 5, 10, 11, 8)## Testing group cal: (9) vs (4, 5, 1, 0, 2, 3, 8, 6, 11, 7, 10)## Testing group mock: (10) vs (0, 3, 1, 2, 6, 4, 7, 5, 11, 8, 9)## Testing group cal: (10) vs (4, 5, 1, 0, 2, 3, 8, 6, 9, 11, 7)## Testing group mock: (11) vs (0, 3, 1, 2, 6, 4, 7, 5, 10, 8, 9)## Testing group cal: (11) vs (4, 5, 1, 0, 2, 3, 8, 6, 9, 7, 10)cluster_marker_list <- conserved_markers %>%
filter(minimump_p_val < 0.05) %>%
group_by(cluster_id) %>%
arrange(desc(mock_avg_log2FC), .by_group = TRUE)#Load scripts from SCType github
source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/gene_sets_prepare.R"); source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/sctype_score_.R")
# load auto-detection function
source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/auto_detect_tissue_type.R")
db_ = "https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/ScTypeDB_full.xlsx";
#Prepare cell type marker database from scType
tissue = "Lung" # e.g. Immune system,Pancreas,Liver,Eye,Kidney,Brain,Lung,Adrenal,Heart,Intestine,Muscle,Placenta,Spleen,Stomach,Thymus
# prepare gene sets
gs_list = gene_sets_prepare(db_, tissue)
# get cell-type by cell matrix
es.max = sctype_score(scRNAseqData = seurat_integrated[["integrated"]]@scale.data,
scaled = TRUE,
gs = gs_list$gs_positive,
gs2 = gs_list$gs_negative)
# merge by cluster
cL_resutls = do.call("rbind", lapply(unique(seurat_integrated@meta.data$seurat_clusters), function(cl){
es.max.cl = sort(rowSums(es.max[ ,rownames(seurat_integrated@meta.data[seurat_integrated@meta.data$seurat_clusters==cl, ])]), decreasing = !0)
head(data.frame(cluster = cl, type = names(es.max.cl), scores = es.max.cl, ncells = sum(seurat_integrated@meta.data$seurat_clusters==cl)), 10)
}))
sctype_scores = cL_resutls %>% group_by(cluster) %>% top_n(n = 1, wt = scores)
# set low-confident (low ScType score) clusters to "unknown"
sctype_scores$type[as.numeric(as.character(sctype_scores$scores)) < sctype_scores$ncells/4] = "Unknown"
sctype_scores[,1:3]## # A tibble: 12 × 3
## # Groups: cluster [12]
## cluster type scores
## <fct> <chr> <dbl>
## 1 0 Airway epithelial cells 6244.
## 2 3 Airway goblet cells 23243.
## 3 1 Basal cells (Airway progenitor cells) 25648.
## 4 2 Clara cells 5810.
## 5 6 Ciliated cells 28190.
## 6 4 Airway goblet cells 11964.
## 7 7 Ciliated cells 2796.
## 8 5 Ciliated cells 1888.
## 9 10 Ciliated cells 696.
## 10 11 Ionocytes 1286.
## 11 8 Basal cells (Airway progenitor cells) 1535.
## 12 9 Ciliated cells 1557.#Merge cell types with cluster IDs on UMAP
seurat_integrated@meta.data$customclassif = ""
for(j in unique(sctype_scores$cluster)){
cl_type = sctype_scores[sctype_scores$cluster==j,];
seurat_integrated@meta.data$customclassif[seurat_integrated@meta.data$seurat_clusters == j] = as.character(cl_type$type[1])
}
seurat_integrated@meta.data$customclassif <- as.factor(seurat_integrated@meta.data$customclassif)
#Rename cell types
seurat_integrated@meta.data <- seurat_integrated@meta.data %>%
mutate(., customclassif = fct_recode(customclassif,
"Club cells" = "Clara cells",
"Secretory and goblet cells" = "Airway goblet cells",
"Basal cells"= "Basal cells (Airway progenitor cells)"))
Idents(seurat_integrated) <- "customclassif"
#pdf("final/results/Integrated UMAP-by sctype annotation.pdf")
DimPlot(seurat_integrated, reduction = "umap", label = FALSE, repel = TRUE, group.by = 'customclassif', order = rev(c("Ciliated cells", "Secretory and goblet cells", "Basal cells", "Airway epithelial cells", "Club cells", "Ionocytes"))) &
ggtitle("Integrated UMAP by cell type") &
scale_color_manual(values = c("#1080FF", "#8000FF", "#FB02FF", "#00C13B", "#48EFDA", "#CC9ECB"))
#dev.off()UMAP of the flow markers (except for SiR-Tubulin because there is no specific gene for microtubule production):
#pdf("final/results/Flow marker feature plots.pdf")
FeaturePlot(seurat_integrated, features = c("NGFR", "CEACAM6", "TSPAN8"))
#dev.off()Here’s a violin plot that shows percent flu reads per cell type:
#pdf("final/results/Percent flu per cell type-below 1 percent.pdf")
VlnPlot(subset(seurat_integrated,
subset = percent.flu < 1), features = c("percent.flu"), group.by = "customclassif", split.by = "condition") &
ggtitle("Percent.flu per cell, excluding cells > 1% flu") &
geom_hline(yintercept = 0.07, linetype = "dotted")## The default behaviour of split.by has changed.
## Separate violin plots are now plotted side-by-side.
## To restore the old behaviour of a single split violin,
## set split.plot = TRUE.
##
## This message will be shown once per session.
#dev.off()Finding variable features between mock and Cal-infected conditions
Ciliated cells
Compare Cal condition to mock condition:
#Read in ISG list
human.isgs <- read.csv("Human ISG list.csv", header = TRUE)
#Filter ISGs to those present in the seurat dataset
human.isgs$present_in_seurat <- human.isgs$isg_name %in% rownames(seurat_integrated)
present_isgs <- subset(human.isgs, present_in_seurat == "TRUE")
#Get list of non-flu genes
non_flu_genes <- rownames(seurat_integrated)[!grepl("Cal-", rownames(seurat_integrated))]
ciliated_flu_markers <- FindMarkers(seurat_integrated,
ident.1 = "cal",
ident.2 = "mock",
group.by = "condition",
subset.ident = "Ciliated cells",
features = non_flu_genes,
only.pos = TRUE) %>%
arrange(-avg_log2FC)
head(ciliated_flu_markers)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## IFIT1 0.00000e+00 0.7415201 0.843 0.306 0.000000e+00
## ISG15 0.00000e+00 0.6722145 0.974 0.731 0.000000e+00
## IFIT3 0.00000e+00 0.6719843 0.805 0.309 0.000000e+00
## MX2 1.19428e-279 0.6299650 0.754 0.300 2.397875e-275
## MX1 0.00000e+00 0.5462514 0.908 0.704 0.000000e+00
## IFIT2 5.80847e-262 0.4762110 0.590 0.116 1.166225e-257Filter down to just those genes in the ISG list:
#Filter by ISGs
ciliated_flu_isgs <- ciliated_flu_markers %>%
filter(row.names(ciliated_flu_markers) %in% c(human.isgs$isg_name) & p_val_adj < 0.05) %>%
arrange(-avg_log2FC)
ciliated_flu_isgs## p_val avg_log2FC pct.1 pct.2 p_val_adj
## IFIT1 0.000000e+00 0.7415201 0.843 0.306 0.000000e+00
## ISG15 0.000000e+00 0.6722145 0.974 0.731 0.000000e+00
## IFIT3 0.000000e+00 0.6719843 0.805 0.309 0.000000e+00
## MX2 1.194280e-279 0.6299650 0.754 0.300 2.397875e-275
## MX1 0.000000e+00 0.5462514 0.908 0.704 0.000000e+00
## IFIT2 5.808470e-262 0.4762110 0.590 0.116 1.166225e-257
## IFI44L 1.410567e-201 0.4706013 0.789 0.478 2.832136e-197
## EPSTI1 6.765627e-147 0.4206467 0.772 0.485 1.358403e-142
## OAS1 3.583450e-216 0.4162229 0.767 0.401 7.194852e-212
## IFITM3 9.208208e-168 0.4016819 0.926 0.747 1.848824e-163
## CXCL10 5.935923e-210 0.3800614 0.383 0.019 1.191815e-205
## HERC6 2.681928e-140 0.3650758 0.779 0.524 5.384776e-136
## STAT1 1.758302e-151 0.3585424 0.836 0.713 3.530319e-147
## IFI44 2.067009e-155 0.3514414 0.790 0.535 4.150141e-151
## RSAD2 1.073202e-156 0.3511265 0.622 0.256 2.154774e-152
## OAS3 6.474145e-156 0.3474076 0.668 0.320 1.299879e-151
## CMPK2 5.119906e-220 0.3467248 0.595 0.161 1.027975e-215
## OAS2 8.746945e-167 0.3466508 0.709 0.351 1.756212e-162
## IFITM1 6.912270e-167 0.3445033 0.603 0.235 1.387846e-162
## SP110 1.378091e-140 0.3429668 0.722 0.436 2.766932e-136
## EIF2AK2 6.633879e-151 0.3358670 0.828 0.689 1.331950e-146
## S100A8 2.577778e-98 0.3322686 0.959 0.847 5.175663e-94
## USP18 1.616055e-145 0.3172172 0.667 0.333 3.244715e-141
## IFI6 6.816007e-105 0.3171491 0.861 0.664 1.368518e-100
## TNFSF10 1.604669e-108 0.3005941 0.873 0.694 3.221854e-104
## XAF1 1.192020e-105 0.2986734 0.771 0.571 2.393338e-101
## IFIH1 9.219606e-115 0.2787151 0.667 0.367 1.851112e-110
## DDX60 6.725654e-89 0.2764911 0.728 0.490 1.350377e-84
## LY6E 7.313879e-87 0.2681172 0.912 0.772 1.468481e-82
## UBE2L6 3.787221e-126 0.2663267 0.648 0.311 7.603983e-122
## IFI35 6.028072e-139 0.2661661 0.619 0.265 1.210316e-134
## IFI16 2.045421e-93 0.2653364 0.831 0.696 4.106796e-89
## NT5C3A 2.459612e-99 0.2646557 0.702 0.430 4.938410e-95
## IRF7 3.168109e-138 0.2633244 0.620 0.263 6.360928e-134
## SP100 2.931219e-89 0.2579815 0.836 0.747 5.885302e-85
## TRIM22 1.929381e-101 0.2572618 0.685 0.413 3.873811e-97
## PARP9 6.624259e-91 0.2563859 0.768 0.558 1.330019e-86
## PNPT1 2.636759e-79 0.2520700 0.716 0.498 5.294085e-75
## SAMD9L 1.789260e-91 0.2512817 0.619 0.332 3.592477e-87Secretory cells
Compare infected vs uninfected:
secretory_flu_markers <- FindMarkers(seurat_integrated,
ident.1 = "cal",
ident.2 = "mock",
group.by = "condition",
subset.ident = "Secretory and goblet cells",
features = non_flu_genes,
only.pos = TRUE) %>%
arrange(-avg_log2FC)
head(secretory_flu_markers)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## IFIT1 0 0.7676341 0.813 0.209 0
## MX2 0 0.7317553 0.782 0.298 0
## IFIT3 0 0.7253454 0.790 0.229 0
## ISG15 0 0.6779783 0.975 0.667 0
## MX1 0 0.6626965 0.937 0.603 0
## IFIT2 0 0.6422126 0.635 0.113 0Filter down to just those genes in the ISG list:
#Filter by ISGs
secretory_flu_isgs <- secretory_flu_markers %>%
filter(row.names(secretory_flu_markers) %in% c(human.isgs$isg_name) & p_val_adj < 0.05)
secretory_flu_isgs## p_val avg_log2FC pct.1 pct.2 p_val_adj
## IFIT1 0.000000e+00 0.7676341 0.813 0.209 0.000000e+00
## MX2 0.000000e+00 0.7317553 0.782 0.298 0.000000e+00
## IFIT3 0.000000e+00 0.7253454 0.790 0.229 0.000000e+00
## ISG15 0.000000e+00 0.6779783 0.975 0.667 0.000000e+00
## MX1 0.000000e+00 0.6626965 0.937 0.603 0.000000e+00
## IFIT2 0.000000e+00 0.6422126 0.635 0.113 0.000000e+00
## EPSTI1 0.000000e+00 0.5870960 0.821 0.399 0.000000e+00
## HERC6 0.000000e+00 0.5086003 0.817 0.439 0.000000e+00
## STAT1 0.000000e+00 0.4810814 0.873 0.543 0.000000e+00
## IFI44L 5.709164e-297 0.4710371 0.852 0.537 1.146286e-292
## OAS1 0.000000e+00 0.4535402 0.831 0.449 0.000000e+00
## RSAD2 0.000000e+00 0.4425627 0.572 0.133 0.000000e+00
## EIF2AK2 0.000000e+00 0.4347879 0.856 0.535 0.000000e+00
## OAS3 1.543363e-305 0.4139528 0.683 0.250 3.098763e-301
## PNPT1 7.211500e-294 0.4112965 0.702 0.290 1.447925e-289
## OAS2 0.000000e+00 0.4086651 0.696 0.235 0.000000e+00
## CXCL10 6.062881e-297 0.3997384 0.363 0.017 1.217305e-292
## WARS1 2.835306e-141 0.3997172 0.701 0.399 5.692728e-137
## IFIH1 1.386410e-251 0.3963246 0.723 0.340 2.783634e-247
## IFI6 1.089501e-211 0.3938571 0.858 0.569 2.187499e-207
## SAMD9L 3.183601e-217 0.3882134 0.707 0.351 6.392033e-213
## IFITM3 8.459682e-267 0.3839391 0.947 0.759 1.698535e-262
## XAF1 1.276330e-241 0.3799012 0.828 0.497 2.562615e-237
## IFITM1 1.181233e-259 0.3761270 0.633 0.246 2.371679e-255
## DDX60 1.086551e-208 0.3732002 0.769 0.430 2.181578e-204
## TNFSF10 6.306160e-197 0.3713709 0.921 0.766 1.266151e-192
## IFI16 5.340292e-260 0.3654602 0.898 0.671 1.072224e-255
## SP110 7.357978e-225 0.3651250 0.747 0.397 1.477335e-220
## USP18 7.778217e-308 0.3566385 0.557 0.138 1.561710e-303
## NT5C3A 1.150961e-208 0.3362132 0.658 0.297 2.310899e-204
## IFI44 4.176083e-215 0.3346687 0.870 0.625 8.384739e-211
## CMPK2 0.000000e+00 0.3320051 0.535 0.098 0.000000e+00
## PML 2.693246e-195 0.3180263 0.746 0.406 5.407499e-191
## OASL 5.363787e-243 0.3153075 0.469 0.109 1.076941e-238
## ZC3HAV1 1.726873e-160 0.3112975 0.800 0.488 3.467216e-156
## PARP9 1.134341e-190 0.3070806 0.779 0.451 2.277529e-186
## TRIM22 7.565332e-172 0.3007388 0.799 0.496 1.518967e-167
## SP100 2.126518e-200 0.2992663 0.931 0.805 4.269622e-196
## SAMHD1 6.626641e-175 0.2972250 0.681 0.346 1.330497e-170
## IFI35 2.448377e-219 0.2936198 0.627 0.248 4.915851e-215
## LAP3 8.723396e-178 0.2819091 0.704 0.359 1.751483e-173
## SPATS2L 1.035438e-162 0.2813626 0.926 0.786 2.078953e-158
## IRF7 9.239318e-227 0.2807078 0.614 0.225 1.855070e-222
## RNF213 1.885203e-113 0.2671630 0.927 0.831 3.785110e-109
## SLC26A4 1.328134e-75 0.2592204 0.696 0.442 2.666628e-71
## S100A8 5.284027e-88 0.2557201 0.962 0.835 1.060927e-83
## HELZ2 2.363514e-176 0.2519776 0.672 0.305 4.745463e-172
## HERC5 1.158457e-213 0.2514386 0.391 0.079 2.325949e-209Dot plot of Cal vs Mock ISGs
rbind(ciliated_flu_isgs %>%
tibble::rownames_to_column(var = "gene") %>%
mutate(cell_type = "ciliated"),
secretory_flu_isgs %>%
tibble::rownames_to_column(var = "gene") %>%
mutate(cell_type = "secretory")) %>%
group_by(cell_type) %>%
mutate(gene = fct_reorder(gene, avg_log2FC)) %>%
ggplot(aes(x = avg_log2FC, y = gene, fill = cell_type)) +
geom_dotplot(binaxis = "y", binwidth = 1, position = "identity") +
labs(x = "Average log2 fold change",
y = "Gene ID") +
ggtitle("ISGs upregulated in Cal vs Mock\nCiliated and secretory cells") +
theme_bw()
#### Expression of common ISGs in ciliated and secretory cells
Find which ISGs are upregulated in Cal vs mock-infected conditions in both ciliated and secretory/goblet cell populations:
common_isgs <- intersect(rownames(ciliated_flu_isgs), rownames(secretory_flu_isgs)) %>%
sort()
Idents(seurat_integrated) <- "customclassif"
#Reorder the cell type levels so that they will be the same between heat maps
seurat_integrated@meta.data$customclassif <- factor(seurat_integrated@meta.data$customclassif, levels = c("Ciliated cells", "Secretory and goblet cells", "Basal cells", "Airway epithelial cells", "Club cells", "Ionocytes"))Markers of ciliated infected vs secretory infected
Are there any genes or pathways that could explain why ciliated cells are seemingly better at producing more flu than secretory cells?
#Created a bin for infected vs uninfected cells, to be used later. Infected cells have flu percentage > 0.07% of all reads
seurat_integrated@meta.data$inf_vs_uninf <- cut(seurat_integrated@meta.data$percent.flu,
breaks=c(-Inf, 0.07, Inf),
labels=c("Uninfected","Infected"))
Idents(seurat_integrated) <- "inf_vs_uninf"
#Markers in Secretory/goblet cells uninfected vs high infection
cal_infected_cil_vs_infected_sec_markers <- FindMarkers(subset(seurat_integrated,
condition == "cal"),
ident.1 = "Ciliated cells",
ident.2 = "Secretory and goblet cells",
group.by = "customclassif",
subset.ident = "Infected",
features = non_flu_genes,
only.pos = TRUE) %>%
arrange(-avg_log2FC) %>%
filter(p_val_adj < 0.05)
cal_infected_cil_vs_infected_sec_markers## p_val avg_log2FC pct.1 pct.2 p_val_adj
## CFAP299 2.011439e-76 0.9001332 0.551 0.250 4.038567e-72
## AGBL4 7.092440e-65 0.8861937 0.498 0.223 1.424020e-60
## DNAH12 3.422434e-89 0.8834466 0.653 0.360 6.871562e-85
## WDR49 1.878765e-68 0.8470607 0.516 0.234 3.772185e-64
## DNAH7 6.355103e-64 0.8436501 0.510 0.248 1.275978e-59
## DNAH6 7.090908e-76 0.8396328 0.525 0.216 1.423713e-71
## RP1 2.954828e-74 0.8305763 0.494 0.183 5.932704e-70
## CFAP54 8.446071e-64 0.8187336 0.480 0.210 1.695802e-59
## PACRG 1.988803e-81 0.8146694 0.516 0.183 3.993119e-77
## HYDIN 8.507839e-70 0.8059888 0.484 0.190 1.708204e-65
## DNAH3 5.718205e-68 0.8024765 0.488 0.199 1.148101e-63
## LRRIQ1 3.198730e-78 0.7997953 0.546 0.234 6.422411e-74
## DCDC1 1.419677e-70 0.7907153 0.431 0.131 2.850428e-66
## CFAP47 4.425237e-65 0.7864814 0.473 0.187 8.884991e-61
## CDHR3 7.737746e-76 0.7857126 0.499 0.179 1.553585e-71
## NEK10 1.382886e-65 0.7787245 0.551 0.289 2.776558e-61
## DNAH9 2.465838e-58 0.7617351 0.471 0.209 4.950910e-54
## ARMC3 1.178116e-84 0.7592489 0.503 0.163 2.365422e-80
## STK33 2.559781e-71 0.7468007 0.480 0.177 5.139528e-67
## SPAG17 4.398318e-68 0.7447436 0.521 0.239 8.830944e-64
## DNAH11 6.813082e-61 0.7400316 0.403 0.130 1.367931e-56
## SPATA17 1.069476e-73 0.7300234 0.497 0.191 2.147294e-69
## TMEM232 3.490709e-48 0.7277205 0.529 0.339 7.008645e-44
## ZBBX 4.796893e-83 0.6958069 0.453 0.112 9.631203e-79
## DTHD1 1.242396e-72 0.6875624 0.471 0.158 2.494483e-68
## RSPH1 1.159061e-82 0.6869641 0.692 0.427 2.327163e-78
## ADGB 1.575571e-71 0.6737527 0.406 0.101 3.163432e-67
## TEKT1 8.289402e-79 0.6637965 0.551 0.230 1.664346e-74
## LMNTD1 1.167135e-73 0.6598154 0.361 0.062 2.343373e-69
## SERPINI2 6.699166e-65 0.6447691 0.410 0.123 1.345059e-60
## CFAP43 3.301934e-68 0.6427881 0.449 0.150 6.629624e-64
## SPEF2 1.796763e-61 0.6383975 0.408 0.132 3.607541e-57
## RFX2 1.512494e-53 0.6331338 0.448 0.196 3.036785e-49
## DNAI1 8.138879e-78 0.6311880 0.409 0.089 1.634124e-73
## TTC29 7.024223e-70 0.6191537 0.403 0.097 1.410324e-65
## CFAP251 2.399774e-72 0.6157407 0.492 0.176 4.818266e-68
## VWA3A 3.887943e-60 0.6121971 0.374 0.106 7.806212e-56
## ODAD2 8.229796e-69 0.6062871 0.377 0.082 1.652378e-64
## CFAP46 5.737540e-70 0.6038550 0.370 0.075 1.151983e-65
## SYNE1 1.004546e-52 0.6033039 0.405 0.151 2.016928e-48
## FANK1 6.249988e-50 0.5959150 0.517 0.310 1.254873e-45
## FHAD1 1.102081e-50 0.5912443 0.427 0.181 2.212758e-46
## SPAG6 4.823414e-60 0.5827526 0.451 0.174 9.684450e-56
## DNAI4 1.652559e-57 0.5785344 0.423 0.155 3.318008e-53
## NEK11 1.314263e-35 0.5765468 0.453 0.285 2.638777e-31
## CFAP95 1.299328e-66 0.5764039 0.494 0.190 2.608790e-62
## KCNE1 2.495738e-62 0.5684768 0.368 0.091 5.010944e-58
## DNAH10 9.342930e-68 0.5637219 0.385 0.090 1.875874e-63
## IGFBP7 4.007759e-56 0.5631366 0.634 0.431 8.046778e-52
## MAP3K19 1.555746e-59 0.5603738 0.388 0.113 3.123627e-55
## LDLRAD4 3.116714e-40 0.5581032 0.354 0.142 6.257739e-36
## CFAP221 4.076872e-67 0.5562846 0.380 0.088 8.185543e-63
## CCDC170 1.705060e-45 0.5489650 0.468 0.249 3.423420e-41
## ROPN1L 3.860791e-54 0.5471285 0.544 0.297 7.751697e-50
## C20orf85 3.899390e-48 0.5441727 0.625 0.431 7.829196e-44
## FAM227A 3.637257e-59 0.5434780 0.349 0.083 7.302884e-55
## CFAP61 1.256690e-65 0.5415661 0.337 0.060 2.523182e-61
## KIAA2012 9.577215e-53 0.5346066 0.320 0.077 1.922913e-48
## EFCAB6 2.076394e-37 0.5324222 0.394 0.194 4.168984e-33
## CAPSL 1.998279e-47 0.5286692 0.492 0.254 4.012145e-43
## CAPS 8.036433e-69 0.5278521 0.802 0.698 1.613555e-64
## PIFO 4.474557e-44 0.5274560 0.537 0.321 8.984016e-40
## DYNC2H1 1.070183e-32 0.5223671 0.418 0.247 2.148714e-28
## ULK4 3.763625e-22 0.5219252 0.508 0.449 7.556606e-18
## CFAP52 1.630592e-65 0.5187495 0.373 0.087 3.273902e-61
## RRAD 1.117609e-41 0.5182708 0.452 0.235 2.243936e-37
## TPPP3 2.621563e-47 0.5170674 0.568 0.346 5.263574e-43
## DNAH5 5.548664e-23 0.5163608 0.510 0.445 1.114061e-18
## TUBA1A 7.423015e-45 0.5141191 0.651 0.488 1.490393e-40
## DNAI7 1.075651e-51 0.5109903 0.373 0.120 2.159692e-47
## DNAAF1 1.985334e-53 0.5070525 0.369 0.112 3.986154e-49
## CCDC30 5.311622e-30 0.5053535 0.381 0.213 1.066468e-25
## MORN2 5.590441e-43 0.5052746 0.638 0.487 1.122449e-38
## EFHC1 8.801936e-37 0.5048267 0.454 0.273 1.767253e-32
## CHD9NB 4.353930e-43 0.5044703 0.472 0.252 8.741821e-39
## SNTN 1.536556e-45 0.5001131 0.480 0.244 3.085097e-41
## CFAP157 7.763257e-60 0.4930458 0.340 0.074 1.558707e-55
## DNAAF4 2.283360e-48 0.4898171 0.389 0.146 4.584530e-44
## CFAP210 1.655194e-56 0.4896942 0.383 0.112 3.323298e-52
## SAXO2 2.033541e-51 0.4852253 0.364 0.113 4.082944e-47
## ERICH3 2.254528e-48 0.4843736 0.337 0.101 4.526641e-44
## CFAP90 2.261090e-43 0.4820197 0.479 0.248 4.539816e-39
## KIF9 2.580541e-47 0.4808343 0.500 0.261 5.181209e-43
## AK7 2.035367e-57 0.4800892 0.364 0.097 4.086609e-53
## DNAI3 6.579425e-61 0.4772220 0.352 0.079 1.321017e-56
## C9orf24 4.727329e-34 0.4745310 0.577 0.429 9.491531e-30
## CIBAR2 1.085673e-49 0.4741593 0.428 0.168 2.179814e-45
## CRIP1 3.521552e-36 0.4729794 0.531 0.352 7.070571e-32
## DNALI1 1.396453e-40 0.4706500 0.476 0.260 2.803799e-36
## TMEM190 2.905813e-31 0.4699985 0.579 0.451 5.834291e-27
## CFAP73 1.496442e-52 0.4677993 0.365 0.110 3.004556e-48
## MOK 2.212841e-40 0.4659248 0.388 0.174 4.442943e-36
## DNAI2 1.042703e-57 0.4649427 0.334 0.072 2.093539e-53
## PPIL6 1.152324e-49 0.4645076 0.397 0.147 2.313636e-45
## FBXL13 1.555125e-25 0.4628466 0.385 0.239 3.122380e-21
## NPHP1 2.132194e-47 0.4576328 0.371 0.129 4.281020e-43
## CKB 4.618089e-44 0.4559826 0.665 0.532 9.272198e-40
## CCDC78 1.670038e-51 0.4558936 0.406 0.143 3.353102e-47
## CABCOCO1 2.469732e-57 0.4550505 0.330 0.068 4.958728e-53
## DNAAF11 7.411236e-46 0.4543336 0.378 0.142 1.488028e-41
## PIERCE1 4.534662e-32 0.4528138 0.475 0.300 9.104694e-28
## LRGUK 2.147287e-33 0.4523340 0.325 0.138 4.311323e-29
## VWA3B 1.407023e-49 0.4504462 0.328 0.090 2.825020e-45
## IQUB 3.842643e-55 0.4499619 0.352 0.091 7.715258e-51
## CFAP276 6.780166e-35 0.4458327 0.451 0.246 1.361322e-30
## AK8 3.659308e-58 0.4452081 0.360 0.090 7.347158e-54
## SPAG16 6.936442e-33 0.4440358 0.658 0.590 1.392699e-28
## CROCC2 7.527866e-54 0.4419945 0.307 0.060 1.511445e-49
## RFX3 1.279718e-15 0.4383696 0.564 0.583 2.569418e-11
## ZMYND10 1.024781e-41 0.4357911 0.427 0.194 2.057554e-37
## IFT57 5.887883e-38 0.4352744 0.496 0.296 1.182169e-33
## CLXN 1.128020e-44 0.4342238 0.381 0.143 2.264838e-40
## KCND2 1.586657e-32 0.4340091 0.242 0.071 3.185690e-28
## RSPH9 2.838722e-43 0.4329596 0.385 0.152 5.699586e-39
## DRC3 4.142818e-47 0.4328472 0.345 0.107 8.317950e-43
## AK9 1.595718e-17 0.4327922 0.426 0.344 3.203882e-13
## ANKFN1 7.957386e-41 0.4323839 0.260 0.062 1.597684e-36
## DRC1 1.565475e-53 0.4307229 0.352 0.094 3.143162e-49
## CFAP144 1.298774e-33 0.4304545 0.442 0.247 2.607679e-29
## CFAP300 5.369733e-47 0.4295950 0.379 0.134 1.078135e-42
## CFAP45 3.008784e-51 0.4249923 0.361 0.106 6.041036e-47
## PLCH1 2.321778e-20 0.4248957 0.336 0.204 4.661667e-16
## CEP126 1.158962e-36 0.4215828 0.358 0.152 2.326964e-32
## FAM229B 7.346693e-30 0.4213383 0.450 0.281 1.475069e-25
## IQCH 1.931551e-29 0.4187132 0.357 0.178 3.878169e-25
## DLEC1 5.840650e-54 0.4183956 0.301 0.059 1.172686e-49
## CETN2 5.270211e-27 0.4168987 0.516 0.385 1.058153e-22
## CFAP77 9.633535e-52 0.4132415 0.319 0.075 1.934221e-47
## CFAP70 3.098361e-27 0.4130761 0.378 0.215 6.220890e-23
## UFC1 6.834321e-42 0.4112022 0.698 0.578 1.372195e-37
## CCDC17 8.654598e-47 0.4085818 0.343 0.104 1.737670e-42
## FAM81B 3.242529e-48 0.4079102 0.349 0.103 6.510349e-44
## AKAP14 6.154224e-41 0.4068367 0.362 0.137 1.235645e-36
## CCDC40 8.497552e-45 0.4050950 0.303 0.080 1.706138e-40
## BBOF1 2.135854e-26 0.4038881 0.418 0.271 4.288367e-22
## SPA17 8.139967e-29 0.4032692 0.442 0.271 1.634343e-24
## FRMPD2 2.614743e-46 0.4018533 0.283 0.064 5.249881e-42
## TUBB4B 6.650213e-77 0.4015578 0.902 0.894 1.335230e-72
## CFAP100 2.839354e-39 0.4007752 0.326 0.116 5.700856e-35
## TTC26 3.327924e-45 0.4000931 0.314 0.089 6.681805e-41
## CFAP69 7.784487e-33 0.3986667 0.305 0.122 1.562969e-28
## C22orf15 1.185590e-36 0.3972059 0.345 0.135 2.380427e-32
## MORN5 9.945909e-40 0.3967354 0.363 0.138 1.996940e-35
## TMEM231 5.842583e-40 0.3961966 0.322 0.110 1.173074e-35
## CFAP44 2.400784e-22 0.3948324 0.358 0.219 4.820293e-18
## CFAP74 3.168679e-53 0.3947185 0.302 0.060 6.362074e-49
## SPATA18 4.407417e-33 0.3946973 0.347 0.157 8.849212e-29
## MS4A8 7.875332e-31 0.3942115 0.365 0.181 1.581209e-26
## RUVBL2 1.018569e-31 0.3922203 0.485 0.307 2.045083e-27
## CC2D2A 5.862243e-30 0.3915691 0.372 0.191 1.177021e-25
## EFHC2 5.096991e-48 0.3890723 0.299 0.068 1.023374e-43
## CFAP126 1.344548e-34 0.3880838 0.347 0.145 2.699584e-30
## LRRC23 2.950162e-29 0.3878928 0.445 0.275 5.923335e-25
## OMG 9.623458e-18 0.3844746 0.496 0.408 1.932198e-13
## NME7 2.957911e-22 0.3829825 0.506 0.416 5.938893e-18
## GRM7 2.692698e-32 0.3800935 0.228 0.060 5.406399e-28
## DIXDC1 2.443624e-20 0.3784840 0.341 0.209 4.906308e-16
## EGLN3 7.301803e-37 0.3780418 0.264 0.072 1.466056e-32
## TMEM67 2.599789e-30 0.3760218 0.314 0.136 5.219856e-26
## CEP128 2.401818e-19 0.3756631 0.342 0.205 4.822370e-15
## IFT22 1.420713e-28 0.3751047 0.401 0.220 2.852507e-24
## BASP1 4.596094e-26 0.3735683 0.527 0.394 9.228037e-22
## ODF3B 8.367495e-23 0.3713232 0.450 0.316 1.680026e-18
## HOATZ 6.007811e-25 0.3704986 0.405 0.237 1.206248e-20
## CFAP20DC 9.266926e-29 0.3674655 0.337 0.154 1.860613e-24
## KIF27 9.372927e-25 0.3670032 0.368 0.214 1.881896e-20
## DZIP3 8.480776e-22 0.3658274 0.374 0.232 1.702770e-17
## C4orf47 1.455345e-33 0.3652741 0.281 0.095 2.922041e-29
## C6orf118 1.316366e-51 0.3647505 0.291 0.055 2.643000e-47
## TTLL9 3.606603e-48 0.3645379 0.276 0.053 7.241337e-44
## DYNLT2B 8.712167e-25 0.3635417 0.421 0.256 1.749229e-20
## PPP1R42 3.264005e-45 0.3633944 0.288 0.066 6.553469e-41
## ARMC2 1.526952e-27 0.3630736 0.327 0.157 3.065814e-23
## IQCG 9.635214e-22 0.3591051 0.410 0.276 1.934558e-17
## NME5 2.671252e-33 0.3575690 0.348 0.147 5.363340e-29
## CLUAP1 2.019449e-25 0.3575090 0.411 0.251 4.054649e-21
## CCDC74A 2.887243e-39 0.3566476 0.344 0.122 5.797006e-35
## PTGES3 6.861202e-36 0.3548477 0.673 0.572 1.377592e-31
## CFAP298 2.050979e-22 0.3485018 0.431 0.291 4.117955e-18
## DIAPH2 5.946625e-08 0.3478435 0.495 0.532 1.193963e-03
## DYNLT5 3.236919e-43 0.3459702 0.248 0.048 6.499087e-39
## MLF1 3.436868e-24 0.3428590 0.361 0.198 6.900543e-20
## RGS22 6.249268e-44 0.3428488 0.240 0.041 1.254728e-39
## IK 1.515846e-21 0.3414161 0.514 0.405 3.043515e-17
## TCTN1 3.353393e-30 0.3413462 0.349 0.167 6.732943e-26
## ARMH1 2.275608e-31 0.3411936 0.263 0.088 4.568965e-27
## ODAD4 1.586909e-46 0.3401251 0.283 0.060 3.186195e-42
## ENKUR 1.961590e-34 0.3399382 0.324 0.122 3.938481e-30
## POLR2I 2.987409e-24 0.3386356 0.591 0.505 5.998119e-20
## ECT2L 1.344914e-33 0.3367472 0.245 0.068 2.700319e-29
## CDC20B 1.298385e-10 0.3364735 0.421 0.352 2.606898e-06
## CCDC39 1.566904e-43 0.3364271 0.248 0.047 3.146030e-39
## CNTRL 2.958057e-21 0.3362657 0.337 0.193 5.939187e-17
## CCDC60 9.076994e-14 0.3327936 0.449 0.384 1.822479e-09
## DENND6B 2.430810e-34 0.3327874 0.316 0.119 4.880581e-30
## KIF6 2.452318e-36 0.3316638 0.266 0.074 4.923765e-32
## MNS1 5.476294e-34 0.3313590 0.313 0.114 1.099530e-29
## CALM2 6.035933e-60 0.3311013 0.875 0.804 1.211895e-55
## DYNLRB2 1.044336e-30 0.3307432 0.318 0.128 2.096819e-26
## ATP5IF1 1.283861e-63 0.3295591 0.899 0.848 2.577735e-59
## WDR54 7.524300e-24 0.3289578 0.345 0.181 1.510729e-19
## BICC1 1.200061e-36 0.3288150 0.216 0.043 2.409483e-32
## DPCD 7.168005e-18 0.3267605 0.403 0.280 1.439192e-13
## NUDC 2.197679e-23 0.3248490 0.614 0.552 4.412499e-19
## DMKN 4.900736e-23 0.3224829 0.420 0.270 9.839699e-19
## LDLRAD1 1.399083e-30 0.3220136 0.308 0.121 2.809079e-26
## TSGA10 1.398719e-12 0.3217006 0.405 0.335 2.808348e-08
## CSPP1 3.853437e-09 0.3209436 0.426 0.406 7.736931e-05
## SRSF9 4.560407e-29 0.3205621 0.621 0.507 9.156385e-25
## PSENEN 6.139879e-16 0.3205233 0.388 0.280 1.232765e-11
## C8orf34 8.998593e-38 0.3200300 0.206 0.034 1.806738e-33
## NQO1 5.440350e-19 0.3199772 0.468 0.357 1.092313e-14
## CFAP36 2.389490e-21 0.3191724 0.403 0.256 4.797618e-17
## FILIP1 4.582436e-37 0.3188664 0.240 0.055 9.200615e-33
## IFT81 1.723808e-18 0.3182678 0.324 0.193 3.461062e-14
## CDHR4 1.057289e-43 0.3157294 0.240 0.041 2.122826e-39
## DZIP1L 7.101871e-41 0.3156713 0.242 0.049 1.425914e-36
## RIBC2 4.301212e-31 0.3156240 0.306 0.114 8.635973e-27
## EFCAB2 8.483204e-12 0.3152755 0.438 0.395 1.703258e-07
## CCDC113 2.043667e-31 0.3147893 0.282 0.099 4.103275e-27
## TTC21A 2.832073e-28 0.3143348 0.289 0.119 5.686236e-24
## TOGARAM2 1.465304e-45 0.3131724 0.247 0.041 2.942038e-41
## DYNLT1 1.029903e-51 0.3125122 0.870 0.828 2.067839e-47
## ANKMY1 2.420933e-12 0.3105268 0.460 0.422 4.860750e-08
## DYNC2I1 2.559170e-12 0.3095964 0.425 0.365 5.138301e-08
## DYNLL1 5.475718e-73 0.3093922 0.955 0.937 1.099415e-68
## SPACA9 6.709442e-23 0.3072089 0.316 0.159 1.347122e-18
## C12orf75 1.651237e-23 0.3060509 0.362 0.197 3.315353e-19
## NEK5 4.462231e-24 0.3058419 0.254 0.103 8.959268e-20
## CEP83 1.000581e-11 0.3048727 0.354 0.270 2.008967e-07
## GPX4 1.602780e-56 0.3039086 0.906 0.848 3.218062e-52
## NWD1 2.025114e-19 0.3035848 0.244 0.112 4.066023e-15
## RUVBL1 3.384051e-22 0.3024250 0.351 0.193 6.794498e-18
## KRT5 3.766690e-41 0.3023023 0.878 0.863 7.562761e-37
## CFAP97D2 2.169079e-34 0.2996692 0.215 0.045 4.355076e-30
## IFT88 1.776064e-15 0.2994884 0.322 0.209 3.565982e-11
## PTRH1 1.419125e-26 0.2975737 0.289 0.122 2.849320e-22
## SMIM22 7.289975e-19 0.2964488 0.593 0.544 1.463681e-14
## TSTD1 2.274315e-20 0.2946850 0.597 0.520 4.566369e-16
## AGPAT4 1.253405e-27 0.2924785 0.269 0.099 2.516586e-23
## SFN 1.331814e-27 0.2923542 0.715 0.681 2.674015e-23
## SPATA6L 6.137681e-09 0.2923032 0.421 0.390 1.232324e-04
## COL21A1 1.347847e-29 0.2917548 0.218 0.059 2.706207e-25
## C5AR1 8.184883e-30 0.2913462 0.261 0.089 1.643361e-25
## MIPEP 7.353381e-15 0.2911933 0.328 0.216 1.476412e-10
## CCDC148 2.097685e-15 0.2905876 0.261 0.146 4.211732e-11
## TNFAIP8L1 1.394917e-35 0.2902118 0.276 0.079 2.800714e-31
## CALM1 1.166527e-42 0.2901170 0.849 0.816 2.342152e-38
## CERKL 1.892473e-47 0.2895111 0.216 0.022 3.799708e-43
## ASB14 1.418675e-33 0.2894503 0.237 0.060 2.848416e-29
## HSBP1 2.239082e-35 0.2876911 0.774 0.720 4.495630e-31
## MROH9 3.525767e-13 0.2871782 0.273 0.164 7.079035e-09
## DRC7 5.086325e-44 0.2863114 0.246 0.043 1.021232e-39
## RPGR 9.072901e-21 0.2850579 0.291 0.148 1.821657e-16
## ZNF440 2.809523e-12 0.2846040 0.334 0.242 5.640959e-08
## ADAM12 4.486672e-34 0.2845529 0.221 0.049 9.008341e-30
## ENSG00000281883 7.574048e-21 0.2844795 0.252 0.112 1.520717e-16
## RSPH3 5.428874e-15 0.2829919 0.376 0.268 1.090009e-10
## TRAF3IP1 5.934479e-11 0.2829621 0.371 0.302 1.191525e-06
## NAP1L1 2.016311e-21 0.2814145 0.608 0.545 4.048348e-17
## UBXN11 4.317159e-31 0.2814088 0.282 0.100 8.667992e-27
## IQCD 5.060593e-30 0.2813437 0.270 0.094 1.016066e-25
## DAW1 1.413857e-38 0.2812284 0.262 0.063 2.838742e-34
## RSPH4A 5.042410e-27 0.2795770 0.258 0.093 1.012415e-22
## CCDC180 2.777754e-39 0.2781467 0.236 0.047 5.577174e-35
## AGR3 4.482489e-13 0.2768824 0.465 0.393 8.999942e-09
## ZFHX2 3.082724e-28 0.2761478 0.245 0.082 6.189493e-24
## CENPK 1.233298e-37 0.2759952 0.264 0.066 2.476216e-33
## ARL3 1.564744e-13 0.2758941 0.424 0.332 3.141693e-09
## AKR7A2 1.184880e-15 0.2749816 0.422 0.307 2.379002e-11
## GRM5 2.071563e-27 0.2742637 0.180 0.040 4.159285e-23
## PKIG 5.210151e-21 0.2737928 0.301 0.152 1.046094e-16
## CCDC153 4.923312e-26 0.2732154 0.280 0.111 9.885026e-22
## KATNAL2 5.810066e-20 0.2726417 0.309 0.164 1.166545e-15
## IQCK 2.316020e-07 0.2722783 0.407 0.392 4.650105e-03
## TUSC3 3.976087e-16 0.2713555 0.408 0.297 7.983187e-12
## FAM216B 1.157641e-24 0.2711225 0.248 0.094 2.324311e-20
## IFT27 7.435052e-16 0.2706725 0.354 0.239 1.492810e-11
## CFAP57 6.078380e-32 0.2693429 0.233 0.062 1.220417e-27
## JAZF1 6.372707e-12 0.2691474 0.311 0.213 1.279512e-07
## IFT122 2.904128e-17 0.2689692 0.280 0.154 5.830907e-13
## TUBA4B 9.087371e-36 0.2674913 0.252 0.064 1.824562e-31
## IFT172 2.804853e-14 0.2673158 0.336 0.225 5.631585e-10
## LRRC74B 3.355591e-42 0.2671726 0.209 0.026 6.737356e-38
## TAGLN2 1.117068e-16 0.2671723 0.562 0.489 2.242849e-12
## S100A10 2.361872e-40 0.2664627 0.888 0.864 4.742166e-36
## RPL36AL 2.424456e-42 0.2664527 0.854 0.807 4.867822e-38
## MT2A 2.832258e-52 0.2662806 0.963 0.946 5.686607e-48
## ENDOG 1.055215e-12 0.2660059 0.401 0.308 2.118661e-08
## CIB1 1.155471e-29 0.2657562 0.774 0.739 2.319954e-25
## CFAP53 2.167654e-27 0.2654598 0.238 0.078 4.352215e-23
## HSPH1 6.700917e-13 0.2652217 0.432 0.353 1.345410e-08
## ARHGAP18 3.644996e-10 0.2643133 0.412 0.361 7.318423e-06
## PRDX5 4.853721e-52 0.2638953 0.929 0.916 9.745302e-48
## CIAO2B 8.926844e-16 0.2636221 0.476 0.372 1.792332e-11
## DNAL1 6.777037e-20 0.2634886 0.301 0.158 1.360693e-15
## FRY 6.892675e-17 0.2633440 0.252 0.126 1.383911e-12
## LRRC49 2.719669e-25 0.2622846 0.272 0.107 5.460551e-21
## PPP1R7 2.817139e-16 0.2622226 0.518 0.429 5.656252e-12
## GON7 4.039982e-19 0.2614320 0.284 0.144 8.111476e-15
## SPATA6 5.915823e-10 0.2607281 0.361 0.288 1.187779e-05
## RAB36 7.794374e-37 0.2606736 0.249 0.060 1.564954e-32
## SPAG1 5.948586e-09 0.2598132 0.506 0.536 1.194357e-04
## OSCP1 1.091019e-21 0.2594645 0.299 0.146 2.190548e-17
## C11orf16 1.650391e-31 0.2591716 0.217 0.053 3.313655e-27
## SNRPG 1.034835e-22 0.2585727 0.682 0.610 2.077741e-18
## FABP5 6.234825e-27 0.2577297 0.839 0.810 1.251828e-22
## TOMM7 6.220607e-31 0.2576706 0.774 0.737 1.248973e-26
## C11orf97 7.584638e-23 0.2574759 0.235 0.089 1.522844e-18
## CARS1 1.441587e-12 0.2564401 0.471 0.393 2.894418e-08
## HSPE1 6.209619e-29 0.2557007 0.773 0.745 1.246767e-24
## METRN 2.199028e-14 0.2555948 0.334 0.221 4.415208e-10
## IQCA1 4.366107e-13 0.2554486 0.332 0.226 8.766270e-09
## CLDN3 3.430832e-08 0.2551553 0.350 0.292 6.888425e-04
## GADD45GIP1 2.937590e-13 0.2547902 0.484 0.412 5.898093e-09
## COX8A 2.304305e-26 0.2546498 0.757 0.680 4.626583e-22
## DNPH1 1.952883e-11 0.2545920 0.411 0.330 3.920998e-07
## ANKRD18A 2.801461e-20 0.2545629 0.262 0.118 5.624774e-16
## TTLL10 1.817272e-26 0.2545278 0.243 0.086 3.648718e-22
## ANKRD18B 4.559048e-37 0.2539596 0.218 0.041 9.153656e-33
## EFHB 1.432737e-42 0.2530179 0.207 0.026 2.876649e-38
## IGFBP2 2.885020e-32 0.2522835 0.757 0.739 5.792542e-28
## DNAH2 2.465001e-31 0.2519203 0.222 0.056 4.949229e-27
## SOD1 1.049411e-21 0.2515416 0.685 0.652 2.107008e-17
## IFT43 3.635238e-12 0.2515025 0.483 0.429 7.298831e-08
## CSTPP1 1.904204e-06 0.2500659 0.489 0.523 3.823261e-02#write.csv(cal_infected_cil_vs_infected_sec_markers, "final/results/cal_infected_cil_vs_infected_sec_markers.csv")Filter the markers found in infected ciliated vs infected secretory to show just ISGs:
#Filter by ISGs
cal_infected_cil_vs_infected_sec_isgs <- cal_infected_cil_vs_infected_sec_markers %>%
filter(row.names(cal_infected_cil_vs_infected_sec_markers) %in% c(human.isgs$isg_name)) %>%
mutate(gene = rownames(.),
comparison = "ciliated_vs_secretory")
cal_infected_cil_vs_infected_sec_isgs## p_val avg_log2FC pct.1 pct.2 p_val_adj gene
## ULK4 3.763625e-22 0.5219252 0.508 0.449 7.556606e-18 ULK4
## ODF3B 8.367495e-23 0.3713232 0.450 0.316 1.680026e-18 ODF3B
## DYNLT1 1.029903e-51 0.3125122 0.870 0.828 2.067839e-47 DYNLT1
## IGFBP2 2.885020e-32 0.2522835 0.757 0.739 5.792542e-28 IGFBP2
## comparison
## ULK4 ciliated_vs_secretory
## ODF3B ciliated_vs_secretory
## DYNLT1 ciliated_vs_secretory
## IGFBP2 ciliated_vs_secretory#write.csv(cal_infected_cil_vs_infected_sec_isgs, "final/results/cal_infected_cil_vs_infected_sec_isgs.csv")Compared to the infected secretory vs ciliated cells (below), there are not many upregulated ISGs in infected ciliated cells.
Markers of secretory/goblet infected vs ciliated infected
I tried the opposite comparison looking at secretory cells versus ciliated cells:
Idents(seurat_integrated) <- "inf_vs_uninf"
#Markers in Secretory/goblet cells uninfected vs high infection
cal_infected_sec_vs_infected_cil_markers <- FindMarkers(subset(seurat_integrated,
condition == "cal"),
ident.1 = "Secretory and goblet cells",
ident.2 = "Ciliated cells",
group.by = "customclassif",
subset.ident = "Infected",
features = non_flu_genes,
only.pos = TRUE) %>%
arrange(-avg_log2FC) %>%
filter(p_val_adj < 0.05)
cal_infected_sec_vs_infected_cil_markers## p_val avg_log2FC pct.1 pct.2 p_val_adj
## CD55 2.643313e-206 0.9204484 0.914 0.529 5.307245e-202
## RIMS1 1.409594e-141 0.9108092 0.750 0.237 2.830184e-137
## THRB 9.294896e-168 0.8665072 0.881 0.414 1.866229e-163
## GPRC5A 2.556545e-126 0.8613923 0.763 0.320 5.133030e-122
## PPM1L 3.877469e-154 0.8476897 0.804 0.296 7.785182e-150
## FER1L6 2.777553e-135 0.8305428 0.729 0.219 5.576770e-131
## BMPR1B 2.650600e-147 0.8166886 0.845 0.378 5.321875e-143
## ARHGAP26 2.322432e-165 0.8149785 0.893 0.466 4.662979e-161
## DPYD 5.438001e-171 0.8102868 0.890 0.442 1.091842e-166
## MAGI3 9.682019e-152 0.7896105 0.865 0.435 1.943956e-147
## GPCPD1 5.515556e-137 0.7805575 0.796 0.358 1.107413e-132
## RARB 4.156822e-128 0.7691211 0.824 0.407 8.346067e-124
## CATSPERB 2.874449e-135 0.7667185 0.769 0.258 5.771319e-131
## INSR 2.262827e-162 0.7644561 0.894 0.516 4.543304e-158
## MAML3 1.379998e-137 0.7513609 0.833 0.343 2.770760e-133
## GMDS 1.230755e-178 0.7414165 0.925 0.606 2.471109e-174
## LYN 1.545128e-175 0.7406463 0.922 0.604 3.102309e-171
## ANTXR1 3.436504e-121 0.7313033 0.684 0.208 6.899813e-117
## MECOM 4.271477e-166 0.7298555 0.934 0.568 8.576272e-162
## SAMD12 2.230148e-154 0.7287055 0.917 0.507 4.477691e-150
## CAPN13 4.376287e-104 0.7199791 0.709 0.287 8.786710e-100
## ATP10B 6.381256e-112 0.7090394 0.743 0.277 1.281229e-107
## RUNX1 2.330846e-189 0.7059437 0.974 0.734 4.679872e-185
## ARHGEF3 4.797006e-129 0.6967845 0.836 0.371 9.631428e-125
## SLC6A14 1.296219e-101 0.6843686 0.762 0.373 2.602548e-97
## EPHA4 2.716086e-106 0.6834627 0.681 0.240 5.453357e-102
## PLEKHG1 6.496166e-101 0.6823663 0.718 0.277 1.304300e-96
## LMO7 5.401582e-118 0.6762250 0.854 0.467 1.084530e-113
## ANKRD36C 6.520629e-114 0.6693407 0.917 0.588 1.309212e-109
## KYNU 1.387067e-123 0.6678688 0.872 0.537 2.784954e-119
## ARL15 1.717854e-108 0.6678205 0.746 0.320 3.449108e-104
## STK17B 1.943839e-104 0.6625862 0.768 0.352 3.902840e-100
## RBPMS 1.816571e-114 0.6598153 0.828 0.417 3.647310e-110
## SRGAP1 8.110617e-116 0.6591228 0.812 0.365 1.628450e-111
## SLC4A4 2.461322e-89 0.6589676 0.542 0.139 4.941843e-85
## IRAK3 1.136483e-116 0.6587056 0.836 0.453 2.281831e-112
## LPP 6.259051e-136 0.6545177 0.899 0.521 1.256692e-131
## LARGE1 1.251307e-106 0.6499431 0.753 0.300 2.512373e-102
## FOXN3 3.439102e-127 0.6489181 0.910 0.534 6.905029e-123
## LIPH 9.691157e-96 0.6463077 0.691 0.258 1.945790e-91
## ZSWIM6 1.243922e-110 0.6371994 0.890 0.523 2.497547e-106
## WSB1 3.113054e-93 0.6271742 0.721 0.340 6.250390e-89
## TJP1 2.860040e-103 0.6238260 0.834 0.446 5.742389e-99
## ETV6 1.664708e-120 0.6194987 0.923 0.603 3.342401e-116
## TPM4 1.134561e-133 0.6180244 0.917 0.628 2.277971e-129
## RALGAPA2 3.174550e-127 0.6160818 0.904 0.559 6.373862e-123
## CDC42BPA 6.393846e-109 0.6143168 0.833 0.452 1.283756e-104
## TCF7L2 3.706915e-101 0.6130464 0.815 0.380 7.442744e-97
## MTUS1 3.505102e-115 0.6129948 0.862 0.499 7.037544e-111
## FHIT 2.223345e-94 0.6125568 0.862 0.477 4.464032e-90
## PPP1R9A 1.550808e-97 0.6120792 0.716 0.269 3.113713e-93
## PTPRK 8.815533e-115 0.6106696 0.873 0.514 1.769983e-110
## DUSP5 1.236791e-108 0.6099508 0.813 0.426 2.483229e-104
## COG5 3.268033e-104 0.6084945 0.800 0.378 6.561557e-100
## FHIP1A 8.543395e-97 0.6084805 0.761 0.336 1.715343e-92
## BACE2 1.052415e-103 0.6075976 0.810 0.446 2.113038e-99
## CAMK2D 2.411623e-103 0.6071738 0.811 0.405 4.842058e-99
## EFNA5 7.842174e-92 0.6028051 0.814 0.437 1.574552e-87
## NAMPT 2.464187e-97 0.6023340 0.826 0.511 4.947596e-93
## B4GALT5 2.310022e-104 0.6008117 0.830 0.481 4.638062e-100
## CHRM3 1.294322e-92 0.5986934 0.532 0.121 2.598740e-88
## PLEKHA5 1.684811e-112 0.5980447 0.924 0.577 3.382764e-108
## TSHZ2 7.594640e-111 0.5966404 0.922 0.598 1.524852e-106
## CAMK1D 1.852981e-103 0.5961946 0.871 0.501 3.720415e-99
## FBXL17 4.975020e-98 0.5956784 0.806 0.388 9.988846e-94
## TET2 2.603217e-96 0.5936733 0.764 0.368 5.226739e-92
## TMTC2 3.121208e-93 0.5932783 0.831 0.442 6.266761e-89
## EYA2 9.868982e-101 0.5901007 0.870 0.501 1.981494e-96
## SLC12A2 7.842295e-80 0.5886538 0.702 0.352 1.574576e-75
## ST6GAL1 6.070442e-91 0.5872018 0.796 0.424 1.218823e-86
## CADPS2 1.133338e-88 0.5867327 0.689 0.304 2.275515e-84
## TRPS1 7.040475e-96 0.5866869 0.761 0.332 1.413587e-91
## NTN1 2.126965e-84 0.5865986 0.765 0.373 4.270521e-80
## STK24 1.431365e-99 0.5843121 0.839 0.484 2.873895e-95
## ERRFI1 4.596194e-80 0.5827655 0.664 0.292 9.228237e-76
## GPHN 2.984687e-92 0.5816340 0.761 0.326 5.992655e-88
## TMEM165 1.321214e-107 0.5810987 0.849 0.534 2.652733e-103
## CHKA 1.109035e-81 0.5806549 0.658 0.270 2.226720e-77
## TMPRSS4 3.997113e-110 0.5804055 0.884 0.562 8.025403e-106
## SIDT1 7.142327e-92 0.5758825 0.697 0.261 1.434036e-87
## NEDD4L 1.401284e-93 0.5733271 0.853 0.481 2.813499e-89
## SEL1L3 2.705698e-91 0.5727730 0.688 0.283 5.432500e-87
## ADAM28 1.017933e-94 0.5716110 0.646 0.217 2.043806e-90
## NAALADL2 3.870421e-83 0.5716002 0.866 0.491 7.771031e-79
## ABLIM1 3.382837e-88 0.5715635 0.796 0.436 6.792061e-84
## PARD3B 2.917700e-85 0.5711388 0.802 0.401 5.858159e-81
## PTK2 2.439050e-107 0.5710468 0.897 0.535 4.897124e-103
## C4orf19 5.222250e-88 0.5705686 0.797 0.396 1.048523e-83
## FBXO34 2.164235e-94 0.5696231 0.828 0.474 4.345351e-90
## HDAC9 3.452742e-72 0.5692110 0.797 0.443 6.932415e-68
## TCF12 2.540867e-88 0.5681658 0.802 0.404 5.101554e-84
## AGAP1 1.659314e-84 0.5673541 0.881 0.498 3.331570e-80
## TANC2 5.017357e-86 0.5663255 0.799 0.398 1.007385e-81
## NHS 4.064224e-77 0.5647562 0.626 0.253 8.160148e-73
## PDLIM5 3.152726e-108 0.5646154 0.885 0.567 6.330044e-104
## MACC1 3.995976e-77 0.5626077 0.833 0.479 8.023120e-73
## PARD3 2.496996e-105 0.5621209 0.913 0.581 5.013469e-101
## SESTD1 2.045460e-87 0.5590093 0.727 0.330 4.106875e-83
## DOCK5 1.880535e-90 0.5585312 0.691 0.274 3.775738e-86
## VMP1 2.676927e-104 0.5585072 0.854 0.591 5.374734e-100
## BAZ2B 1.764730e-91 0.5569197 0.798 0.411 3.543226e-87
## SBF2 1.180976e-89 0.5565843 0.819 0.428 2.371163e-85
## MCTP1 5.259890e-88 0.5564489 0.582 0.175 1.056081e-83
## NFAT5 2.238394e-80 0.5544820 0.771 0.403 4.494248e-76
## SSH2 3.770928e-86 0.5539383 0.788 0.409 7.571270e-82
## HIPK2 7.310156e-86 0.5528480 0.801 0.441 1.467733e-81
## SMYD3 1.499817e-85 0.5506632 0.836 0.483 3.011332e-81
## FNDC3B 9.764366e-87 0.5433548 0.860 0.526 1.960489e-82
## CDH26 1.460045e-74 0.5432777 0.674 0.297 2.931479e-70
## AKAP13 2.130328e-97 0.5432747 0.888 0.552 4.277272e-93
## SGMS1 1.199496e-85 0.5427760 0.750 0.355 2.408347e-81
## F3 1.335275e-73 0.5416496 0.763 0.456 2.680964e-69
## CUX1 2.194315e-94 0.5411313 0.860 0.508 4.405746e-90
## ERBB4 4.145595e-67 0.5408836 0.724 0.355 8.323526e-63
## MAP3K5 8.865464e-80 0.5407392 0.736 0.361 1.780008e-75
## PATJ 2.650538e-85 0.5381556 0.819 0.459 5.321751e-81
## SH3PXD2A 6.561713e-82 0.5363760 0.735 0.339 1.317461e-77
## SPIDR 7.540152e-85 0.5355577 0.888 0.534 1.513912e-80
## TUFT1 9.346845e-81 0.5351931 0.650 0.255 1.876660e-76
## GRAMD2B 2.889457e-88 0.5333590 0.873 0.565 5.801452e-84
## CDKAL1 5.748068e-79 0.5330102 0.776 0.381 1.154097e-74
## TNIP3 3.791628e-54 0.5324533 0.526 0.219 7.612832e-50
## FCHSD2 4.136658e-75 0.5320746 0.739 0.359 8.305582e-71
## MLLT3 5.373743e-80 0.5316785 0.667 0.267 1.078940e-75
## CP 1.164030e-94 0.5311382 0.852 0.571 2.337140e-90
## UGCG 2.261653e-74 0.5289201 0.689 0.331 4.540946e-70
## LDLR 8.237375e-85 0.5285458 0.846 0.537 1.653900e-80
## RBM47 3.789356e-90 0.5283219 0.888 0.557 7.608269e-86
## TMEM117 1.309953e-80 0.5277063 0.656 0.249 2.630124e-76
## ELAPOR1 1.270286e-85 0.5275391 0.491 0.101 2.550480e-81
## PAM 2.526799e-81 0.5275217 0.646 0.264 5.073307e-77
## MACROD2 7.398970e-68 0.5261358 0.722 0.356 1.485565e-63
## RERE 2.451306e-87 0.5233912 0.874 0.538 4.921733e-83
## MED13L 1.895589e-81 0.5228815 0.827 0.452 3.805964e-77
## DOCK4 2.650125e-68 0.5223524 0.700 0.336 5.320921e-64
## RDH10 1.048649e-88 0.5214398 0.843 0.547 2.105476e-84
## PACS1 4.749893e-72 0.5212099 0.725 0.362 9.536836e-68
## MUC5B 6.236569e-75 0.5207695 0.838 0.583 1.252178e-70
## PKP4 7.113038e-77 0.5207506 0.759 0.388 1.428156e-72
## SH3PXD2B 6.669046e-86 0.5189422 0.567 0.162 1.339011e-81
## FAM172A 2.971860e-77 0.5180999 0.780 0.410 5.966901e-73
## KIAA1217 2.037026e-100 0.5170806 0.941 0.672 4.089941e-96
## RRBP1 2.798149e-81 0.5169591 0.768 0.430 5.618123e-77
## NT5C2 2.369949e-81 0.5164803 0.831 0.492 4.758383e-77
## IGF2BP2 2.870188e-76 0.5159413 0.827 0.429 5.762764e-72
## NR3C2 6.065402e-78 0.5158619 0.549 0.164 1.217811e-73
## VPS37B 3.522927e-75 0.5148755 0.760 0.407 7.073333e-71
## KIF13B 5.484374e-78 0.5146382 0.700 0.312 1.101153e-73
## ARHGEF38 1.367532e-70 0.5142822 0.777 0.359 2.745730e-66
## SLC5A8 6.919987e-50 0.5137744 0.448 0.164 1.389395e-45
## NR4A1 3.069377e-78 0.5136483 0.647 0.245 6.162696e-74
## MUC4 8.744968e-128 0.5113989 0.958 0.762 1.755815e-123
## CCNL1 1.436928e-75 0.5112416 0.745 0.400 2.885064e-71
## JMJD1C 7.972182e-81 0.5107713 0.866 0.526 1.600655e-76
## OSBPL10 2.037752e-73 0.5103466 0.696 0.318 4.091399e-69
## AHCYL2 8.004534e-70 0.5076972 0.718 0.352 1.607150e-65
## PPP3CA 6.938002e-75 0.5070803 0.690 0.293 1.393012e-70
## SLC45A4 3.849914e-70 0.5069145 0.594 0.234 7.729858e-66
## LMTK2 3.046890e-76 0.5064985 0.656 0.275 6.117546e-72
## CBLB 8.261261e-68 0.5050602 0.736 0.370 1.658696e-63
## CHL1 3.430207e-73 0.5045404 0.522 0.159 6.887170e-69
## GLUD1 1.624834e-59 0.5032596 0.625 0.311 3.262342e-55
## GAN 7.271635e-55 0.5026875 0.560 0.243 1.459999e-50
## LURAP1L 1.872005e-73 0.5024262 0.554 0.181 3.758611e-69
## IMMP2L 3.328664e-69 0.5022994 0.769 0.401 6.683291e-65
## BIRC6 3.901118e-77 0.5012113 0.799 0.432 7.832665e-73
## PKN2 2.951544e-74 0.5010617 0.729 0.377 5.926109e-70
## OXSR1 1.155540e-71 0.5001310 0.624 0.252 2.320094e-67
## PICALM 9.891103e-72 0.4993813 0.743 0.367 1.985936e-67
## RAI14 6.690168e-73 0.4987031 0.624 0.249 1.343252e-68
## AFF4 2.379824e-75 0.4980566 0.704 0.324 4.778210e-71
## ENSG00000272297 2.883355e-78 0.4979016 0.476 0.108 5.789201e-74
## KLF3 6.978861e-66 0.4976716 0.710 0.387 1.401216e-61
## VCL 4.347884e-59 0.4965906 0.739 0.412 8.729681e-55
## KDM6A 1.703629e-73 0.4962585 0.739 0.359 3.420547e-69
## DENND1B 6.801268e-76 0.4954598 0.646 0.269 1.365559e-71
## GNAQ 1.444608e-83 0.4947849 0.887 0.577 2.900483e-79
## ATP13A4 3.151661e-74 0.4927558 0.572 0.200 6.327905e-70
## EPS8 5.649945e-71 0.4924562 0.836 0.493 1.134396e-66
## TANC1 3.160434e-72 0.4918336 0.728 0.347 6.345519e-68
## STK3 2.714766e-71 0.4916981 0.764 0.404 5.450708e-67
## GCNT2 2.653154e-66 0.4912519 0.747 0.384 5.327003e-62
## SVIL 7.478596e-68 0.4901958 0.679 0.314 1.501552e-63
## SEMA3A 8.370513e-67 0.4899129 0.639 0.276 1.680632e-62
## UBAP1 1.591973e-72 0.4887341 0.682 0.310 3.196364e-68
## CYRIB 1.397984e-72 0.4883747 0.768 0.407 2.806872e-68
## OSMR 4.669583e-62 0.4881424 0.621 0.286 9.375590e-58
## URB1 8.850687e-71 0.4874206 0.592 0.226 1.777041e-66
## NEBL 6.909409e-62 0.4870244 0.852 0.488 1.387271e-57
## GRIP1 1.124642e-64 0.4870017 0.702 0.326 2.258056e-60
## DHRS9 1.842976e-78 0.4862305 0.841 0.561 3.700327e-74
## TC2N 8.699948e-68 0.4859987 0.694 0.332 1.746776e-63
## EPB41L4A 4.548284e-66 0.4853790 0.772 0.417 9.132045e-62
## STAT3 8.989100e-68 0.4852114 0.757 0.411 1.804831e-63
## KMT2C 5.672101e-72 0.4849633 0.761 0.395 1.138845e-67
## NPC1 3.068403e-68 0.4849439 0.615 0.257 6.160740e-64
## SPECC1 9.359102e-69 0.4847511 0.663 0.301 1.879120e-64
## ELL2 4.629581e-66 0.4842382 0.728 0.392 9.295273e-62
## B4GALNT3 2.309848e-64 0.4839100 0.517 0.182 4.637713e-60
## RAB11FIP1 1.477098e-71 0.4829832 0.909 0.620 2.965718e-67
## NAV1 3.888139e-71 0.4829079 0.543 0.174 7.806605e-67
## MYH14 6.919371e-64 0.4797516 0.768 0.432 1.389271e-59
## PER2 4.546752e-68 0.4788221 0.689 0.313 9.128968e-64
## ERN2 1.642174e-77 0.4787522 0.609 0.213 3.297158e-73
## VPS13D 1.263776e-67 0.4783601 0.700 0.350 2.537410e-63
## MBD5 2.130138e-66 0.4776017 0.800 0.436 4.276891e-62
## CHODL 7.411382e-54 0.4775314 0.494 0.176 1.488057e-49
## SYTL2 1.133188e-61 0.4758019 0.739 0.399 2.275214e-57
## SERINC5 2.451124e-65 0.4745732 0.664 0.299 4.921366e-61
## IRS2 4.009310e-65 0.4744986 0.625 0.264 8.049893e-61
## KSR1 5.425645e-63 0.4741711 0.531 0.185 1.089361e-58
## EXT1 2.384967e-64 0.4736187 0.880 0.550 4.788538e-60
## LIMK2 2.127206e-65 0.4705217 0.787 0.436 4.271005e-61
## SLC26A4 4.249497e-38 0.4702646 0.606 0.348 8.532139e-34
## TMCC1 6.416665e-66 0.4696769 0.716 0.347 1.288338e-61
## EHMT1 3.588447e-65 0.4694179 0.699 0.349 7.204883e-61
## ALDH1A3 7.553219e-50 0.4692762 0.624 0.338 1.516535e-45
## NCOA1 6.305346e-70 0.4680132 0.671 0.300 1.265987e-65
## UNC13B 3.076090e-71 0.4673357 0.607 0.234 6.176174e-67
## MEIS1 2.066323e-62 0.4671400 0.715 0.362 4.148764e-58
## CARMIL1 5.967616e-62 0.4669569 0.723 0.382 1.198178e-57
## AHR 3.939205e-58 0.4667611 0.718 0.420 7.909136e-54
## FAAH2 3.433306e-59 0.4663074 0.715 0.365 6.893392e-55
## LNX1 1.792409e-67 0.4650875 0.558 0.199 3.598799e-63
## CLINT1 5.186423e-75 0.4632839 0.814 0.512 1.041330e-70
## KCNK1 4.619206e-61 0.4622186 0.656 0.302 9.274441e-57
## ATP8B1 1.035185e-62 0.4620212 0.751 0.404 2.078445e-58
## ZFAND3 1.519022e-62 0.4613521 0.831 0.499 3.049892e-58
## PITPNC1 9.358177e-61 0.4608825 0.631 0.283 1.878935e-56
## GABRP 4.429845e-61 0.4602821 0.627 0.282 8.894243e-57
## SGPP2 1.955172e-60 0.4602570 0.741 0.414 3.925595e-56
## MRTFB 1.035527e-60 0.4601892 0.718 0.380 2.079130e-56
## BMPR2 4.602297e-62 0.4582922 0.714 0.363 9.240492e-58
## GOLM1 5.135083e-64 0.4582171 0.790 0.497 1.031022e-59
## MYOF 5.664207e-64 0.4565777 0.806 0.492 1.137260e-59
## DCLRE1C 1.300220e-60 0.4557708 0.589 0.255 2.610582e-56
## ITPKC 1.513754e-49 0.4557127 0.667 0.356 3.039316e-45
## WIPI1 1.193647e-65 0.4547001 0.593 0.230 2.396604e-61
## ASPH 4.555029e-63 0.4545456 0.747 0.437 9.145588e-59
## TOX3 5.383765e-69 0.4533754 0.505 0.143 1.080952e-64
## FGFR2 3.252714e-61 0.4523328 0.594 0.249 6.530799e-57
## EML4 1.395676e-63 0.4519885 0.705 0.356 2.802238e-59
## CCSER1 3.519561e-54 0.4514453 0.566 0.230 7.066575e-50
## ARHGAP32 1.930336e-63 0.4506958 0.793 0.463 3.875729e-59
## LRBA 6.041867e-63 0.4501687 0.870 0.550 1.213086e-58
## FGD4 1.863305e-60 0.4499830 0.707 0.361 3.741143e-56
## SIPA1L1 1.504595e-60 0.4487978 0.825 0.484 3.020925e-56
## FCGBP 7.348890e-59 0.4479187 0.881 0.727 1.475510e-54
## AFF1 3.717392e-56 0.4475085 0.750 0.436 7.463780e-52
## PTPRG 3.146745e-61 0.4470782 0.549 0.202 6.318034e-57
## PLAC8 7.038492e-71 0.4463895 0.886 0.675 1.413188e-66
## PSD3 4.072865e-60 0.4463271 0.849 0.510 8.177499e-56
## FUT8 5.685766e-55 0.4457735 0.661 0.323 1.141588e-50
## FBXO32 4.083355e-48 0.4455542 0.531 0.245 8.198561e-44
## ZMYM2 9.430471e-64 0.4451637 0.704 0.342 1.893450e-59
## NECTIN2 7.131977e-62 0.4449944 0.669 0.329 1.431958e-57
## KDM4C 5.358098e-65 0.4439383 0.723 0.345 1.075799e-60
## BCL6 2.131871e-61 0.4438743 0.630 0.294 4.280370e-57
## ANK3 1.047604e-67 0.4437989 0.941 0.674 2.103379e-63
## ACACA 1.507091e-57 0.4436673 0.725 0.385 3.025937e-53
## MAML2 2.157258e-57 0.4433717 0.905 0.585 4.331343e-53
## PARP8 8.332253e-57 0.4429153 0.734 0.388 1.672950e-52
## MBNL1 2.228456e-56 0.4429094 0.753 0.440 4.474293e-52
## TXNDC11 2.060387e-66 0.4427050 0.517 0.169 4.136845e-62
## PTPN3 6.134841e-64 0.4408597 0.612 0.263 1.231753e-59
## CFTR 3.448065e-70 0.4403143 0.468 0.114 6.923026e-66
## MACF1 1.967431e-59 0.4402238 0.807 0.477 3.950208e-55
## ZNF609 9.059364e-55 0.4399357 0.821 0.460 1.818939e-50
## WNK2 4.103942e-61 0.4399089 0.584 0.235 8.239895e-57
## RNF152 9.075763e-60 0.4395360 0.545 0.198 1.822232e-55
## BACH1 7.185642e-58 0.4391370 0.644 0.294 1.442733e-53
## GALNT7 2.225702e-58 0.4387145 0.674 0.334 4.468764e-54
## MAP3K8 4.903155e-53 0.4384186 0.726 0.397 9.844555e-49
## STK39 7.207298e-59 0.4383745 0.802 0.461 1.447081e-54
## EXOC6B 1.890691e-58 0.4381128 0.739 0.381 3.796130e-54
## PCDH7 4.180447e-52 0.4378251 0.767 0.432 8.393501e-48
## ARID1B 1.182480e-56 0.4378247 0.851 0.495 2.374184e-52
## ENSG00000283321 5.917702e-61 0.4370493 0.509 0.170 1.188156e-56
## IKZF2 1.110505e-53 0.4368204 0.757 0.417 2.229672e-49
## SCNN1A 1.456301e-64 0.4366580 0.625 0.265 2.923962e-60
## ST3GAL1 3.907462e-66 0.4360347 0.502 0.152 7.845401e-62
## NFIB 7.042057e-53 0.4359876 0.685 0.370 1.413904e-48
## SEC31A 1.183046e-62 0.4357487 0.753 0.446 2.375321e-58
## ASH1L 9.180544e-56 0.4353496 0.771 0.427 1.843270e-51
## NRXN3 2.769857e-52 0.4347915 0.549 0.222 5.561320e-48
## SH3RF1 4.366909e-57 0.4347395 0.675 0.327 8.767880e-53
## KDM7A 7.189890e-60 0.4344448 0.653 0.299 1.443586e-55
## TNFRSF10B 1.055623e-56 0.4341536 0.615 0.275 2.119480e-52
## TMEM178B 2.464704e-64 0.4340876 0.422 0.098 4.948633e-60
## PIP5K1B 9.230892e-60 0.4339648 0.598 0.247 1.853379e-55
## HS3ST1 7.780296e-49 0.4325008 0.543 0.251 1.562128e-44
## XKR6 6.992496e-60 0.4316555 0.635 0.279 1.403953e-55
## USP34 3.384598e-58 0.4316398 0.766 0.434 6.795596e-54
## ARFGEF3 1.845919e-59 0.4310865 0.632 0.285 3.706237e-55
## LYPD6B 1.186403e-53 0.4308921 0.593 0.277 2.382060e-49
## SETD5 1.134267e-58 0.4306638 0.727 0.384 2.277382e-54
## GAREM1 1.714236e-62 0.4300226 0.575 0.227 3.441843e-58
## SASH1 4.737857e-60 0.4298426 0.577 0.237 9.512669e-56
## RNF145 1.016247e-57 0.4274879 0.687 0.357 2.040421e-53
## CHST9 4.265503e-65 0.4262504 0.935 0.705 8.564277e-61
## MAFF 2.293224e-60 0.4261447 0.647 0.295 4.604336e-56
## TCF4 8.204413e-52 0.4255136 0.666 0.359 1.647282e-47
## KLF6 2.618620e-59 0.4251170 0.797 0.548 5.257665e-55
## EZR 1.512153e-87 0.4238404 0.930 0.745 3.036100e-83
## OPHN1 1.848289e-69 0.4234655 0.517 0.154 3.710994e-65
## NF1 8.378893e-55 0.4228414 0.767 0.428 1.682314e-50
## DOCK9 3.843450e-59 0.4228259 0.697 0.336 7.716878e-55
## EPAS1 3.283580e-50 0.4223312 0.766 0.512 6.592772e-46
## RABGAP1 1.413590e-57 0.4222796 0.692 0.347 2.838205e-53
## PTPN2 1.558180e-55 0.4222605 0.644 0.319 3.128514e-51
## NCOA3 3.579600e-55 0.4217191 0.697 0.356 7.187122e-51
## MCTP2 3.802538e-58 0.4212262 0.588 0.248 7.634735e-54
## PDE8A 4.333926e-60 0.4205614 0.565 0.224 8.701656e-56
## MYO1D 2.573884e-53 0.4205103 0.833 0.515 5.167844e-49
## STAG1 7.109208e-54 0.4192562 0.726 0.381 1.427387e-49
## GULP1 4.988835e-49 0.4191830 0.606 0.285 1.001658e-44
## ABCD3 1.717170e-55 0.4190933 0.673 0.356 3.447735e-51
## PSME4 3.007906e-52 0.4184859 0.736 0.413 6.039274e-48
## MSI2 3.331731e-57 0.4181474 0.885 0.589 6.689449e-53
## ARID5B 7.572283e-52 0.4179509 0.721 0.425 1.520363e-47
## TOP1 1.398027e-50 0.4170696 0.752 0.479 2.806959e-46
## RESF1 5.436345e-47 0.4168047 0.563 0.269 1.091509e-42
## DLG1 2.561430e-50 0.4167949 0.825 0.498 5.142838e-46
## STK38 1.325893e-53 0.4164927 0.718 0.398 2.662129e-49
## ATOSA 5.715494e-57 0.4158818 0.635 0.298 1.147557e-52
## CTBP2 1.272689e-52 0.4156029 0.837 0.511 2.555305e-48
## ZMYND8 9.037213e-52 0.4154310 0.751 0.428 1.814492e-47
## NBEA 1.587991e-46 0.4142291 0.762 0.436 3.188368e-42
## ZNF644 1.247463e-50 0.4133917 0.745 0.424 2.504656e-46
## RREB1 4.309246e-54 0.4130958 0.675 0.356 8.652104e-50
## GTDC1 6.936988e-60 0.4129758 0.545 0.208 1.392808e-55
## RGS17 1.130916e-73 0.4124945 0.445 0.090 2.270653e-69
## PLEKHS1 5.304463e-54 0.4116487 0.832 0.589 1.065030e-49
## CDC14B 3.175266e-54 0.4115921 0.613 0.278 6.375299e-50
## NOTCH2 7.957159e-63 0.4115410 0.553 0.205 1.597638e-58
## RAP1B 1.344918e-58 0.4098290 0.802 0.525 2.700327e-54
## TBC1D5 2.930690e-51 0.4088604 0.758 0.435 5.884239e-47
## HOOK2 2.941223e-59 0.4077701 0.579 0.247 5.905387e-55
## FNBP4 3.074753e-52 0.4075647 0.685 0.352 6.173490e-48
## GRHL2 1.544469e-47 0.4064523 0.790 0.467 3.100986e-43
## CREBBP 5.853928e-58 0.4056524 0.660 0.311 1.175352e-53
## FOSL2 9.248636e-56 0.4055974 0.564 0.235 1.856941e-51
## SEC24D 5.735078e-58 0.4049816 0.539 0.208 1.151489e-53
## ZBTB7C 3.146820e-53 0.4049764 0.574 0.244 6.318186e-49
## ZNF827 1.495919e-58 0.4049322 0.584 0.240 3.003507e-54
## SLC15A2 6.168509e-53 0.4044870 0.644 0.333 1.238513e-48
## MYO5B 9.350859e-50 0.4035819 0.716 0.380 1.877465e-45
## NUMB 7.510188e-51 0.4029141 0.809 0.478 1.507896e-46
## USP15 1.873414e-52 0.4026915 0.669 0.337 3.761440e-48
## ZCCHC7 1.731979e-50 0.4022801 0.655 0.334 3.477468e-46
## NR4A3 8.345949e-41 0.4017215 0.361 0.117 1.675700e-36
## CTNNA1 5.308838e-76 0.4011339 0.876 0.649 1.065909e-71
## TRIO 3.368644e-47 0.4006260 0.790 0.454 6.763564e-43
## PTPN13 4.928211e-46 0.4002576 0.689 0.386 9.894862e-42
## VPS13B 8.386945e-51 0.4001773 0.755 0.413 1.683931e-46
## SMURF1 1.723571e-49 0.3998434 0.696 0.363 3.460585e-45
## RASA2 1.551620e-51 0.3991481 0.602 0.282 3.115343e-47
## ECE1 2.242001e-52 0.3991319 0.637 0.316 4.501489e-48
## CHD9 4.553922e-52 0.3987892 0.787 0.463 9.143364e-48
## ATP2C2 4.744600e-48 0.3977229 0.703 0.363 9.526208e-44
## ITSN2 3.368050e-54 0.3976498 0.575 0.249 6.762370e-50
## TCF7L1 3.697196e-55 0.3972319 0.491 0.173 7.423231e-51
## SH3RF2 1.819840e-52 0.3967573 0.604 0.268 3.653874e-48
## TMC5 1.874003e-58 0.3964974 0.920 0.705 3.762624e-54
## PLS3 1.854298e-42 0.3961512 0.609 0.326 3.723060e-38
## SH3KBP1 2.680629e-53 0.3955617 0.583 0.247 5.382167e-49
## USP54 6.911799e-47 0.3948542 0.712 0.366 1.387751e-42
## MBOAT1 3.288341e-46 0.3948270 0.660 0.341 6.602330e-42
## KDM2A 6.040450e-49 0.3946616 0.732 0.376 1.212801e-44
## ARHGEF10L 9.613276e-53 0.3932703 0.558 0.239 1.930153e-48
## RNF19A 3.339359e-45 0.3928830 0.714 0.395 6.704766e-41
## FBXW7 1.239204e-52 0.3917740 0.645 0.301 2.488074e-48
## VTI1A 6.398317e-50 0.3910777 0.732 0.377 1.284654e-45
## NOS1AP 1.060016e-56 0.3904238 0.548 0.216 2.128301e-52
## GSK3B 1.900781e-49 0.3903096 0.685 0.360 3.816388e-45
## PRSS23 1.121881e-40 0.3900241 0.595 0.349 2.252512e-36
## GALNT2 3.077846e-52 0.3893638 0.594 0.273 6.179699e-48
## DENND4C 3.096759e-52 0.3888528 0.648 0.315 6.217672e-48
## CDH1 1.590564e-46 0.3887594 0.724 0.429 3.193534e-42
## CPD 1.757601e-43 0.3876119 0.698 0.452 3.528912e-39
## EPB41L5 2.336173e-51 0.3873094 0.532 0.226 4.690569e-47
## SLC25A25 2.063414e-52 0.3870701 0.527 0.216 4.142923e-48
## EHD4 3.584920e-43 0.3868267 0.644 0.351 7.197802e-39
## DTNB 7.602812e-56 0.3861145 0.534 0.203 1.526493e-51
## ARFGEF1 1.260584e-48 0.3857833 0.717 0.403 2.531000e-44
## NCOA2 5.594879e-47 0.3856232 0.751 0.414 1.123340e-42
## PHACTR2 9.150411e-47 0.3856163 0.645 0.334 1.837219e-42
## PTCHD4 5.203674e-45 0.3855158 0.501 0.204 1.044794e-40
## SOS1 3.252612e-47 0.3853077 0.674 0.356 6.530594e-43
## DOCK1 1.080619e-46 0.3847315 0.765 0.422 2.169667e-42
## PCCA 1.463299e-47 0.3841638 0.695 0.363 2.938013e-43
## ZNF431 1.883575e-57 0.3841271 0.509 0.192 3.781842e-53
## ALCAM 6.199837e-63 0.3836948 0.942 0.733 1.244803e-58
## ST14 3.237340e-44 0.3832307 0.710 0.437 6.499930e-40
## FHL2 4.592917e-44 0.3831525 0.630 0.327 9.221658e-40
## QSOX1 1.233958e-51 0.3824175 0.574 0.261 2.477540e-47
## RNF213 6.435192e-40 0.3819029 0.793 0.516 1.292058e-35
## GABRE 3.825683e-61 0.3816181 0.471 0.139 7.681206e-57
## ITFG1 2.217836e-45 0.3811647 0.749 0.427 4.452972e-41
## UBE2E2 2.552344e-44 0.3808428 0.567 0.277 5.124597e-40
## NIPBL 8.263401e-48 0.3804038 0.733 0.397 1.659126e-43
## ATP11A 5.054290e-51 0.3804034 0.488 0.187 1.014800e-46
## ZBTB20 3.671352e-41 0.3801526 0.845 0.519 7.371340e-37
## ATXN7 8.582775e-53 0.3801409 0.600 0.266 1.723250e-48
## RPS6KA3 3.543911e-46 0.3800223 0.618 0.310 7.115465e-42
## GNE 7.070649e-45 0.3798689 0.493 0.212 1.419645e-40
## ADIPOR2 2.446581e-46 0.3795007 0.556 0.257 4.912246e-42
## NFKB1 1.101263e-44 0.3788119 0.659 0.348 2.211116e-40
## SREBF2 1.129953e-44 0.3783285 0.715 0.414 2.268719e-40
## MYO1E 2.642941e-37 0.3782198 0.842 0.567 5.306496e-33
## MYO1B 1.873386e-42 0.3781766 0.699 0.426 3.761385e-38
## ACER3 2.805859e-55 0.3780397 0.528 0.200 5.633604e-51
## SWAP70 2.555599e-48 0.3779366 0.618 0.310 5.131131e-44
## RAB10 2.354849e-46 0.3778987 0.808 0.550 4.728066e-42
## RASEF 5.713870e-50 0.3771853 0.541 0.220 1.147231e-45
## ANKRD12 2.971449e-46 0.3771235 0.735 0.435 5.966075e-42
## CBFA2T2 6.786008e-48 0.3769251 0.615 0.299 1.362495e-43
## WDFY3 1.796029e-47 0.3763915 0.716 0.380 3.606066e-43
## TUT7 2.173182e-50 0.3762334 0.599 0.285 4.363315e-46
## FNBP1L 1.741747e-44 0.3757673 0.572 0.282 3.497079e-40
## RAD51B 1.842326e-42 0.3756705 0.767 0.422 3.699023e-38
## MX2 3.896435e-30 0.3753024 0.664 0.470 7.823263e-26
## TAOK3 2.536506e-48 0.3746924 0.626 0.295 5.092796e-44
## MCU 1.154076e-40 0.3746206 0.736 0.417 2.317153e-36
## CEMIP2 1.227060e-52 0.3745072 0.459 0.158 2.463692e-48
## CHD2 9.579765e-55 0.3736891 0.833 0.595 1.923425e-50
## NRIP1 4.712665e-47 0.3736560 0.574 0.271 9.462090e-43
## RBFOX2 3.072508e-47 0.3734003 0.751 0.448 6.168981e-43
## NBEAL1 2.611038e-44 0.3733433 0.734 0.432 5.242442e-40
## ALPK1 3.390947e-47 0.3731243 0.672 0.330 6.808343e-43
## PPM1H 2.173392e-44 0.3727023 0.696 0.350 4.363737e-40
## ABCA5 5.843524e-49 0.3727012 0.583 0.270 1.173263e-44
## B4GALT1 1.011051e-36 0.3726490 0.692 0.437 2.029988e-32
## DIP2B 4.510959e-48 0.3725688 0.606 0.295 9.057103e-44
## MYO6 1.458714e-45 0.3716167 0.673 0.357 2.928807e-41
## DEPTOR 2.743673e-57 0.3715184 0.397 0.098 5.508746e-53
## ANKRD17 2.046621e-43 0.3706420 0.734 0.429 4.109205e-39
## ZNHIT6 1.620856e-53 0.3705460 0.433 0.137 3.254355e-49
## CMTM7 1.448689e-49 0.3703597 0.501 0.199 2.908678e-45
## ATP12A 2.429174e-35 0.3703331 0.670 0.418 4.877296e-31
## ARHGEF28 2.073458e-42 0.3701822 0.760 0.443 4.163089e-38
## SOS2 1.248145e-52 0.3701070 0.543 0.226 2.506026e-48
## MFSD4A 1.299282e-57 0.3699640 0.450 0.134 2.608698e-53
## PSEN1 1.919328e-45 0.3698340 0.660 0.350 3.853626e-41
## FUT6 1.082884e-67 0.3696439 0.478 0.121 2.174214e-63
## C1orf21 2.881735e-47 0.3695181 0.543 0.239 5.785947e-43
## HSD17B12 8.840433e-46 0.3688212 0.681 0.362 1.774982e-41
## TLE1 3.831002e-45 0.3685727 0.477 0.198 7.691886e-41
## SLMAP 4.882417e-44 0.3683453 0.755 0.434 9.802917e-40
## RIOK3 2.034124e-43 0.3682852 0.660 0.359 4.084114e-39
## LRMDA 8.591025e-37 0.3677571 0.660 0.387 1.724906e-32
## COP1 2.264350e-42 0.3675864 0.719 0.407 4.546363e-38
## KLF5 1.076713e-54 0.3675315 0.841 0.624 2.161824e-50
## KANSL1 2.100825e-45 0.3671844 0.667 0.342 4.218036e-41
## NDEL1 6.337182e-55 0.3670042 0.528 0.209 1.272379e-50
## ATAD2B 1.160613e-51 0.3669179 0.532 0.226 2.330280e-47
## LRP5 2.407228e-57 0.3663148 0.465 0.149 4.833231e-53
## PUM1 3.901574e-43 0.3659995 0.746 0.439 7.833580e-39
## HERC1 2.366930e-54 0.3657204 0.536 0.213 4.752321e-50
## N4BP2L2 8.633980e-43 0.3656541 0.772 0.466 1.733530e-38
## PLA2R1 2.735165e-49 0.3654036 0.561 0.243 5.491665e-45
## ERBIN 2.978049e-42 0.3651111 0.733 0.447 5.979327e-38
## ATP9B 1.195416e-51 0.3648624 0.555 0.238 2.400156e-47
## VTCN1 1.266543e-43 0.3644341 0.505 0.215 2.542964e-39
## CERS6 5.702698e-40 0.3643721 0.799 0.494 1.144988e-35
## MAPK8 1.767324e-47 0.3643643 0.567 0.258 3.548433e-43
## SPAG9 7.313710e-42 0.3643527 0.660 0.361 1.468447e-37
## ENSG00000282278 4.760134e-47 0.3641867 0.523 0.219 9.557397e-43
## BLTP1 1.468734e-50 0.3632582 0.592 0.268 2.948925e-46
## FRMD4A 5.240628e-41 0.3626113 0.420 0.163 1.052213e-36
## ZNF710 7.391596e-49 0.3625606 0.530 0.220 1.484085e-44
## SMIM31 1.247180e-48 0.3625450 0.480 0.176 2.504089e-44
## FRYL 9.271633e-44 0.3619494 0.695 0.375 1.861558e-39
## NFKBIZ 2.164350e-42 0.3617170 0.653 0.364 4.345581e-38
## RANBP17 1.392882e-41 0.3602406 0.622 0.314 2.796629e-37
## INPP4B 1.534283e-37 0.3600515 0.769 0.425 3.080533e-33
## PPP1R13B 1.734099e-44 0.3598536 0.574 0.274 3.481725e-40
## TNRC18 4.813291e-46 0.3597144 0.566 0.266 9.664125e-42
## ATRX 1.345318e-43 0.3595902 0.726 0.436 2.701129e-39
## SSBP2 5.121575e-39 0.3595672 0.681 0.396 1.028310e-34
## MEIS2 2.181241e-40 0.3592559 0.651 0.340 4.379495e-36
## CHD6 1.112059e-44 0.3588737 0.616 0.309 2.232792e-40
## SLC7A11 9.047559e-42 0.3585332 0.389 0.135 1.816569e-37
## COBLL1 1.306268e-46 0.3581898 0.536 0.235 2.622725e-42
## HOOK3 3.744203e-48 0.3580356 0.569 0.262 7.517611e-44
## SUMF1 1.194657e-43 0.3579174 0.627 0.315 2.398632e-39
## MCL1 2.818341e-44 0.3576880 0.784 0.516 5.658665e-40
## ERO1A 9.279343e-41 0.3568046 0.589 0.299 1.863107e-36
## GPD2 4.229334e-47 0.3566182 0.580 0.265 8.491657e-43
## MAN1A2 4.295540e-42 0.3562358 0.669 0.359 8.624585e-38
## MET 1.829253e-39 0.3560404 0.626 0.342 3.672775e-35
## PLEKHA7 2.453178e-41 0.3553378 0.802 0.483 4.925491e-37
## LIMA1 3.352829e-41 0.3553020 0.740 0.473 6.731811e-37
## RBM6 6.362196e-43 0.3552716 0.701 0.399 1.277402e-38
## SHLD2 2.741837e-47 0.3551772 0.559 0.249 5.505061e-43
## RAPGEF5 6.775437e-43 0.3550652 0.500 0.217 1.360372e-38
## ARL5B 1.165326e-42 0.3550537 0.571 0.289 2.339742e-38
## FAM107B 6.092155e-38 0.3547421 0.688 0.406 1.223183e-33
## CDC42EP4 1.190552e-36 0.3541996 0.555 0.293 2.390390e-32
## CTTNBP2NL 1.212726e-47 0.3539464 0.554 0.243 2.434911e-43
## ROBO1 1.148054e-41 0.3538813 0.520 0.236 2.305062e-37
## PDE4D 5.038336e-36 0.3535614 0.882 0.577 1.011597e-31
## MON2 2.205068e-49 0.3535115 0.587 0.264 4.427337e-45
## TACC2 1.371694e-37 0.3533332 0.747 0.462 2.754088e-33
## SCAPER 6.222684e-46 0.3533043 0.611 0.293 1.249390e-41
## GLG1 3.813035e-46 0.3530843 0.637 0.322 7.655811e-42
## CAPN8 3.660066e-65 0.3529741 0.350 0.048 7.348680e-61
## ARAP2 5.497624e-40 0.3528539 0.656 0.360 1.103813e-35
## STRN 6.212855e-45 0.3519044 0.573 0.276 1.247417e-40
## PRKCE 8.003223e-41 0.3517711 0.699 0.371 1.606887e-36
## NHSL1 3.504347e-40 0.3517017 0.703 0.379 7.036027e-36
## GCH1 3.385188e-44 0.3515024 0.451 0.176 6.796779e-40
## PISD 1.614425e-48 0.3514000 0.494 0.199 3.241442e-44
## TPCN1 7.824459e-48 0.3512855 0.534 0.225 1.570995e-43
## TBL1XR1 1.026809e-39 0.3508274 0.776 0.475 2.061627e-35
## FAM83E 1.398173e-52 0.3507873 0.539 0.205 2.807252e-48
## RAB3GAP1 3.370430e-44 0.3505299 0.655 0.342 6.767149e-40
## MAP4K3 2.431744e-43 0.3503640 0.639 0.325 4.882456e-39
## SRGAP3 1.016933e-36 0.3501411 0.757 0.448 2.041797e-32
## PHLPP1 6.926667e-41 0.3500008 0.639 0.335 1.390736e-36
## ANO10 2.095976e-48 0.3496565 0.502 0.200 4.208300e-44
## PIAS1 8.313830e-45 0.3494024 0.582 0.283 1.669251e-40
## PRKN 3.653917e-43 0.3493152 0.510 0.214 7.336334e-39
## DIAPH1 1.743319e-43 0.3491869 0.557 0.269 3.500235e-39
## MAST4 3.320446e-36 0.3489866 0.698 0.425 6.666792e-32
## YPEL2 9.009369e-43 0.3489707 0.426 0.158 1.808901e-38
## MTAP 2.569534e-41 0.3484359 0.615 0.318 5.159111e-37
## MRTFA 4.025599e-44 0.3479508 0.600 0.293 8.082598e-40
## TMEM45A 1.912055e-33 0.3475271 0.480 0.237 3.839025e-29
## ZMYM4 9.644861e-42 0.3473688 0.619 0.328 1.936495e-37
## TTC17 2.120247e-41 0.3472763 0.682 0.367 4.257032e-37
## REL 2.237081e-42 0.3466965 0.606 0.313 4.491612e-38
## ARHGAP21 3.663420e-41 0.3462764 0.594 0.305 7.355416e-37
## SND1 2.214256e-39 0.3459243 0.733 0.445 4.445783e-35
## ZHX2 3.705834e-47 0.3459119 0.496 0.200 7.440573e-43
## ARID2 5.788386e-43 0.3458414 0.607 0.308 1.162192e-38
## PBRM1 4.767419e-42 0.3446522 0.649 0.355 9.572025e-38
## ZFHX3 5.217188e-39 0.3444567 0.636 0.345 1.047507e-34
## TULP4 6.733886e-39 0.3442472 0.708 0.394 1.352030e-34
## AP3B1 5.969628e-43 0.3441826 0.641 0.332 1.198582e-38
## SMCHD1 4.305617e-37 0.3440204 0.611 0.338 8.644817e-33
## SUSD4 1.208462e-54 0.3439706 0.397 0.099 2.426351e-50
## FRK 1.303402e-40 0.3437980 0.655 0.351 2.616970e-36
## CRYBG1 2.134038e-38 0.3437648 0.640 0.360 4.284722e-34
## LITAF 9.485306e-38 0.3436772 0.734 0.476 1.904460e-33
## KAT6B 3.138373e-42 0.3435183 0.635 0.329 6.301226e-38
## PHACTR4 1.029561e-37 0.3430273 0.710 0.406 2.067153e-33
## SLC38A1 8.071365e-44 0.3430052 0.560 0.258 1.620569e-39
## NFX1 1.108799e-42 0.3429100 0.586 0.289 2.226246e-38
## PBX1 6.618798e-37 0.3428963 0.751 0.465 1.328922e-32
## GOLGA4 1.215293e-39 0.3421558 0.705 0.432 2.440065e-35
## EIF2AK3 3.069882e-46 0.3408525 0.421 0.148 6.163710e-42
## PLD1 1.094853e-42 0.3407152 0.557 0.257 2.198247e-38
## JUP 2.441133e-38 0.3402274 0.754 0.483 4.901306e-34
## ARGLU1 1.378967e-40 0.3401912 0.657 0.368 2.768690e-36
## CREB3L1 1.074865e-52 0.3401823 0.417 0.116 2.158115e-48
## NSMCE2 1.001981e-36 0.3400264 0.721 0.430 2.011777e-32
## UBE2H 4.913907e-37 0.3389891 0.787 0.520 9.866143e-33
## RCOR1 6.311020e-38 0.3386623 0.625 0.346 1.267127e-33
## EVA1C 2.159344e-33 0.3386350 0.776 0.484 4.335531e-29
## JARID2 1.914744e-41 0.3376194 0.579 0.282 3.844423e-37
## ZNF618 1.893224e-51 0.3373276 0.413 0.126 3.801216e-47
## PPP6R3 3.210055e-39 0.3372771 0.677 0.381 6.445148e-35
## HELZ 1.328461e-43 0.3372162 0.560 0.264 2.667283e-39
## HERC4 1.001368e-40 0.3371416 0.640 0.337 2.010546e-36
## MTMR3 2.996120e-42 0.3367750 0.593 0.296 6.015610e-38
## ARIH1 1.923276e-37 0.3366079 0.698 0.402 3.861554e-33
## RAI1 3.716190e-45 0.3366013 0.501 0.213 7.461367e-41
## UMAD1 1.118115e-40 0.3365497 0.573 0.282 2.244952e-36
## MAPKAPK2 1.186271e-42 0.3365283 0.526 0.241 2.381794e-38
## USP6NL 5.720257e-44 0.3361323 0.595 0.270 1.148513e-39
## PTPRZ1 1.845778e-37 0.3356913 0.623 0.331 3.705953e-33
## SP100 3.664178e-37 0.3354059 0.799 0.545 7.356937e-33
## NRTN 2.141024e-55 0.3353760 0.343 0.066 4.298747e-51
## CPNE8 5.190139e-39 0.3349857 0.600 0.306 1.042076e-34
## IL1RAPL2 5.664121e-38 0.3347581 0.233 0.038 1.137242e-33
## R3HDM2 4.415017e-44 0.3345852 0.604 0.294 8.864471e-40
## PARP14 1.786327e-32 0.3338429 0.780 0.548 3.586588e-28
## PHF14 3.269890e-41 0.3330924 0.589 0.297 6.565286e-37
## SCNN1B 4.628842e-52 0.3328814 0.429 0.127 9.293788e-48
## TMEM62 1.444537e-44 0.3326102 0.463 0.182 2.900341e-40
## STIM1 5.706620e-39 0.3324175 0.634 0.341 1.145775e-34
## BABAM2 2.092845e-40 0.3320591 0.631 0.330 4.202015e-36
## PLCB4 1.456080e-35 0.3319579 0.596 0.300 2.923518e-31
## NCOA7 3.890245e-77 0.3318847 0.925 0.813 7.810833e-73
## STXBP5 2.296394e-43 0.3317443 0.555 0.256 4.610700e-39
## ADAMTSL3 3.440916e-38 0.3315241 0.387 0.141 6.908670e-34
## CLIP1 1.783874e-34 0.3310101 0.756 0.474 3.581663e-30
## MUC16 5.898120e-48 0.3305338 0.930 0.757 1.184225e-43
## APBB2 1.001580e-43 0.3302063 0.500 0.207 2.010972e-39
## CCSER2 1.993860e-40 0.3298343 0.606 0.311 4.003272e-36
## BCAS1 3.374638e-38 0.3298100 0.622 0.305 6.775598e-34
## MKLN1 1.712085e-40 0.3297547 0.655 0.348 3.437525e-36
## SLC38A2 5.727461e-34 0.3296650 0.673 0.412 1.149960e-29
## DMD 4.059047e-30 0.3295927 0.708 0.435 8.149754e-26
## MBP 7.612050e-36 0.3294537 0.726 0.441 1.528347e-31
## ABL1 4.118826e-42 0.3283255 0.531 0.241 8.269779e-38
## SLC39A11 7.037748e-40 0.3282924 0.561 0.275 1.413039e-35
## PLA2G6 1.663628e-42 0.3282732 0.447 0.174 3.340233e-38
## MIPOL1 2.359689e-38 0.3277871 0.623 0.332 4.737783e-34
## ADAM10 1.861181e-36 0.3276068 0.633 0.356 3.736879e-32
## NLK 3.957837e-44 0.3275007 0.540 0.235 7.946545e-40
## ARID4B 7.209044e-36 0.3273430 0.717 0.452 1.447432e-31
## STAU2 8.998257e-41 0.3269242 0.645 0.334 1.806670e-36
## BMPR1A 6.064976e-38 0.3268185 0.622 0.322 1.217726e-33
## STK38L 2.315840e-37 0.3267260 0.482 0.220 4.649744e-33
## PACSIN2 3.594400e-40 0.3266854 0.595 0.307 7.216835e-36
## TRA2A 7.373015e-37 0.3266047 0.672 0.403 1.480354e-32
## PPP1R12A 1.275573e-39 0.3263325 0.628 0.328 2.561095e-35
## UBR5 9.236223e-38 0.3263181 0.656 0.361 1.854449e-33
## FAR1 4.890970e-40 0.3261994 0.553 0.274 9.820089e-36
## SCFD2 1.404581e-42 0.3261186 0.480 0.198 2.820117e-38
## BBX 2.204277e-35 0.3260497 0.716 0.435 4.425748e-31
## ABCA13 2.185457e-37 0.3259897 0.962 0.775 4.387960e-33
## ACYP2 5.218426e-36 0.3258934 0.676 0.378 1.047756e-31
## ERBB3 2.164941e-38 0.3255934 0.549 0.278 4.346768e-34
## FAM168A 1.682756e-41 0.3255136 0.464 0.196 3.378637e-37
## MARCHF3 1.206506e-37 0.3253867 0.502 0.224 2.422424e-33
## CDIN1 1.339552e-42 0.3251463 0.468 0.196 2.689552e-38
## SLK 5.953564e-37 0.3247817 0.608 0.328 1.195357e-32
## RSF1 3.490014e-38 0.3246946 0.643 0.356 7.007251e-34
## PDSS2 1.937233e-37 0.3245057 0.672 0.377 3.889576e-33
## CDK6 2.873883e-34 0.3245005 0.629 0.351 5.770183e-30
## TTC6 4.571209e-37 0.3244971 0.612 0.306 9.178074e-33
## WWTR1 1.389072e-32 0.3242974 0.648 0.378 2.788979e-28
## SUSD6 7.863914e-40 0.3242315 0.526 0.248 1.578917e-35
## CRADD 2.804656e-35 0.3241686 0.542 0.277 5.631189e-31
## PAK1 2.669110e-39 0.3237694 0.610 0.323 5.359038e-35
## CCNY 1.716153e-37 0.3234559 0.602 0.318 3.445693e-33
## USP9X 1.086238e-37 0.3231848 0.605 0.317 2.180949e-33
## GLIS3 5.157086e-31 0.3222955 0.713 0.410 1.035440e-26
## UBA6 7.235613e-38 0.3222166 0.591 0.308 1.452766e-33
## RNF13 7.647467e-38 0.3219833 0.569 0.294 1.535459e-33
## CHRNA7 3.610740e-58 0.3216405 0.341 0.057 7.249645e-54
## MYH9 1.594018e-31 0.3212669 0.699 0.456 3.200470e-27
## RABGAP1L 7.852805e-31 0.3201303 0.910 0.658 1.576686e-26
## ACTR3 8.224155e-37 0.3200812 0.744 0.501 1.651246e-32
## SIPA1L3 1.375557e-35 0.3200277 0.707 0.396 2.761842e-31
## MEF2A 4.425226e-35 0.3193910 0.682 0.389 8.884969e-31
## RBMS3 4.183736e-41 0.3193158 0.418 0.156 8.400104e-37
## FUT3 1.567646e-45 0.3192297 0.442 0.161 3.147519e-41
## CYB5R3 1.447178e-34 0.3191045 0.592 0.336 2.905644e-30
## CASK 4.648228e-36 0.3184504 0.618 0.330 9.332712e-32
## FA2H 1.313996e-38 0.3183858 0.380 0.132 2.638241e-34
## CTSC 5.013713e-37 0.3183195 0.829 0.661 1.006653e-32
## ERC1 5.953258e-36 0.3182234 0.634 0.340 1.195295e-31
## CTNND1 1.631126e-40 0.3182033 0.549 0.266 3.274975e-36
## LARP4B 1.013934e-36 0.3180764 0.669 0.373 2.035777e-32
## ANKRD10 1.522386e-40 0.3178670 0.551 0.271 3.056646e-36
## EXOC6 8.947332e-46 0.3178591 0.482 0.188 1.796445e-41
## DNM2 1.407735e-40 0.3175666 0.602 0.304 2.826451e-36
## CMYA5 7.187198e-47 0.3172650 0.406 0.131 1.443046e-42
## IGF1R 2.907389e-31 0.3170079 0.787 0.472 5.837455e-27
## CYTH1 7.490202e-37 0.3169111 0.644 0.348 1.503883e-32
## PTBP2 7.450148e-44 0.3166962 0.519 0.225 1.495841e-39
## TNRC6B 3.499944e-32 0.3164135 0.783 0.496 7.027187e-28
## SMAD3 4.167571e-31 0.3164063 0.653 0.387 8.367649e-27
## BAIAP2L1 1.002869e-33 0.3162299 0.717 0.417 2.013560e-29
## ITSN1 3.976113e-39 0.3153364 0.542 0.256 7.983240e-35
## VWA8 1.631828e-39 0.3145492 0.612 0.300 3.276384e-35
## TBL1X 1.248069e-36 0.3140486 0.545 0.273 2.505874e-32
## PRKAA2 3.326329e-47 0.3138496 0.373 0.108 6.678604e-43
## SEC24A 5.907996e-42 0.3137434 0.453 0.184 1.186208e-37
## EPB41L1 1.576313e-36 0.3136825 0.537 0.265 3.164920e-32
## TICAM1 1.533246e-38 0.3126981 0.467 0.204 3.078451e-34
## NAV2 4.316491e-31 0.3125940 0.719 0.419 8.666650e-27
## NUP98 1.306064e-40 0.3121577 0.567 0.271 2.622315e-36
## MICAL2 3.249386e-38 0.3121569 0.490 0.222 6.524116e-34
## WDR70 5.058713e-37 0.3121161 0.662 0.361 1.015688e-32
## SMARCC1 2.917311e-34 0.3120617 0.643 0.366 5.857377e-30
## PDGFC 4.344508e-42 0.3120580 0.355 0.111 8.722904e-38
## LRIG1 1.167487e-37 0.3119356 0.524 0.247 2.344080e-33
## PRR5L 8.105356e-49 0.3118125 0.411 0.123 1.627393e-44
## SNX9 6.729098e-37 0.3116464 0.512 0.243 1.351068e-32
## RNF130 1.812470e-32 0.3113075 0.624 0.362 3.639077e-28
## DNAAF9 6.984192e-35 0.3109292 0.620 0.324 1.402286e-30
## FAF1 1.850808e-31 0.3103072 0.813 0.535 3.716053e-27
## MAPK10 3.517804e-25 0.3099295 0.922 0.654 7.063047e-21
## RAB6A 5.079547e-36 0.3099107 0.573 0.311 1.019871e-31
## ACSL3 5.751627e-30 0.3094532 0.652 0.408 1.154812e-25
## FARS2 1.127727e-34 0.3092587 0.706 0.398 2.264249e-30
## RAPGEF2 1.928253e-33 0.3088202 0.662 0.361 3.871546e-29
## PDS5A 3.145743e-37 0.3083985 0.616 0.329 6.316023e-33
## NKTR 6.613541e-39 0.3080364 0.544 0.264 1.327867e-34
## ZNF516 1.040447e-40 0.3076739 0.535 0.245 2.089010e-36
## DYRK1A 2.808280e-34 0.3075084 0.657 0.364 5.638464e-30
## PCMTD1 1.047738e-35 0.3073767 0.602 0.327 2.103647e-31
## GSPT1 1.129113e-31 0.3071145 0.618 0.375 2.267034e-27
## PTPN12 2.445403e-33 0.3068855 0.538 0.284 4.909880e-29
## ATL2 3.825195e-40 0.3065886 0.479 0.214 7.680228e-36
## PAFAH1B1 8.334540e-33 0.3065632 0.702 0.448 1.673409e-28
## TES 1.237615e-35 0.3052776 0.573 0.301 2.484884e-31
## ANKIB1 3.789842e-31 0.3050096 0.635 0.371 7.609245e-27
## BICDL2 2.641745e-37 0.3049362 0.609 0.311 5.304097e-33
## CDYL 3.219347e-35 0.3048899 0.570 0.296 6.463806e-31
## RORA 1.084049e-31 0.3038126 0.453 0.216 2.176553e-27
## BHLHE40 3.559718e-32 0.3032653 0.578 0.324 7.147201e-28
## TRIP12 1.256316e-31 0.3030395 0.730 0.457 2.522432e-27
## ZDHHC14 1.052946e-41 0.3027755 0.359 0.112 2.114105e-37
## DAPK1 4.214077e-41 0.3025344 0.376 0.124 8.461023e-37
## TMCO4 1.036647e-38 0.3024211 0.490 0.221 2.081381e-34
## TRAK1 6.379582e-35 0.3023378 0.675 0.381 1.280893e-30
## N4BP1 1.585815e-30 0.3020420 0.578 0.326 3.184000e-26
## SPPL3 2.056233e-34 0.3018847 0.585 0.310 4.128505e-30
## GOLGB1 3.714172e-32 0.3013911 0.708 0.447 7.457314e-28
## CD2AP 2.600325e-33 0.3013429 0.629 0.356 5.220933e-29
## VPS13C 3.977072e-31 0.3011709 0.681 0.407 7.985165e-27
## TRIM38 1.390488e-32 0.3011002 0.570 0.309 2.791822e-28
## MAGT1 2.511463e-36 0.3010004 0.523 0.256 5.042515e-32
## RSBN1L 4.869747e-39 0.3009096 0.543 0.263 9.777478e-35
## ARF4 4.342320e-30 0.3007023 0.716 0.499 8.718511e-26
## XPR1 7.796168e-36 0.3006031 0.586 0.301 1.565315e-31
## XPO6 4.051432e-41 0.3000613 0.468 0.194 8.134466e-37
## FMN1 2.921923e-32 0.2998540 0.635 0.348 5.866636e-28
## EPB41 7.627762e-39 0.2994395 0.451 0.188 1.531502e-34
## MTHFD1L 8.604376e-34 0.2989456 0.435 0.194 1.727587e-29
## PREP 5.211543e-39 0.2988368 0.429 0.171 1.046374e-34
## OXR1 6.813637e-34 0.2987479 0.595 0.308 1.368042e-29
## STRN3 6.995517e-34 0.2987157 0.638 0.348 1.404560e-29
## SMAP1 5.696132e-37 0.2985382 0.564 0.285 1.143669e-32
## ZNF292 7.488664e-33 0.2980588 0.639 0.354 1.503574e-28
## ABCA7 3.548580e-39 0.2979359 0.509 0.232 7.124839e-35
## CLIC6 7.400183e-30 0.2977927 0.667 0.391 1.485809e-25
## EPS8L1 7.376397e-42 0.2977846 0.512 0.220 1.481033e-37
## FOXJ3 3.664469e-38 0.2976598 0.512 0.242 7.357522e-34
## ZNF254 1.794848e-36 0.2976275 0.568 0.283 3.603695e-32
## NFE2L2 2.613550e-43 0.2975427 0.903 0.742 5.247485e-39
## TAOK1 6.689770e-36 0.2973815 0.600 0.311 1.343172e-31
## FAM135A 5.343585e-31 0.2966052 0.559 0.304 1.072885e-26
## HSH2D 2.761643e-34 0.2965751 0.516 0.257 5.544827e-30
## RBMS1 2.045637e-32 0.2956528 0.627 0.353 4.107230e-28
## BRWD1 8.816784e-38 0.2956333 0.568 0.279 1.770234e-33
## MID1 1.778484e-29 0.2954610 0.697 0.399 3.570840e-25
## CAPZB 1.797717e-28 0.2952756 0.723 0.489 3.609457e-24
## PAN3 1.560191e-30 0.2948714 0.701 0.400 3.132551e-26
## RICTOR 2.698952e-33 0.2945636 0.580 0.298 5.418956e-29
## RNF24 1.400828e-38 0.2942892 0.426 0.170 2.812582e-34
## RIN2 2.712441e-29 0.2942298 0.718 0.435 5.446039e-25
## RARRES1 1.674247e-22 0.2940268 0.746 0.602 3.361553e-18
## MYO9A 1.274129e-35 0.2939898 0.518 0.244 2.558197e-31
## ZNF638 2.610146e-33 0.2938779 0.618 0.340 5.240651e-29
## PIP4K2A 7.570875e-35 0.2938637 0.433 0.189 1.520080e-30
## SQSTM1 1.054348e-25 0.2938066 0.722 0.546 2.116919e-21
## PTPRJ 2.848152e-32 0.2935412 0.559 0.296 5.718519e-28
## BPTF 3.455964e-29 0.2934905 0.735 0.472 6.938885e-25
## CPEB4 5.026274e-32 0.2934526 0.587 0.319 1.009175e-27
## BAZ1A 7.324969e-30 0.2934370 0.659 0.397 1.470707e-25
## WNT2B 1.211097e-37 0.2930323 0.378 0.137 2.431641e-33
## MAP7 1.740470e-32 0.2928018 0.669 0.372 3.494516e-28
## ATG7 1.428140e-31 0.2925742 0.652 0.369 2.867419e-27
## ERGIC1 6.372302e-34 0.2925555 0.566 0.301 1.279431e-29
## IDO1 3.425650e-18 0.2922384 0.574 0.399 6.878020e-14
## MORC3 2.811177e-32 0.2921938 0.614 0.339 5.644282e-28
## NIBAN2 3.574184e-33 0.2921564 0.514 0.260 7.176247e-29
## ARFGAP3 5.907815e-35 0.2917113 0.535 0.267 1.186171e-30
## CMTM8 3.602802e-34 0.2913764 0.477 0.226 7.233705e-30
## KIAA0319L 1.589781e-35 0.2913437 0.555 0.281 3.191963e-31
## EXOC4 5.640351e-26 0.2908994 0.850 0.573 1.132470e-21
## USP47 6.023366e-36 0.2905208 0.532 0.265 1.209371e-31
## HECW2 1.813546e-41 0.2904709 0.321 0.083 3.641237e-37
## NR6A1 3.979846e-33 0.2903116 0.584 0.300 7.990735e-29
## RELCH 4.351069e-38 0.2901649 0.524 0.248 8.736076e-34
## TTC39B 5.948309e-35 0.2897474 0.549 0.269 1.194301e-30
## ZNF148 1.906359e-32 0.2894066 0.616 0.343 3.827588e-28
## LCOR 2.760631e-32 0.2889158 0.603 0.333 5.542794e-28
## PDXDC1 4.656333e-34 0.2884774 0.595 0.329 9.348985e-30
## CA3 7.775038e-46 0.2884499 0.342 0.089 1.561072e-41
## TLE4 6.307941e-27 0.2883497 0.686 0.416 1.266508e-22
## UBR3 4.562576e-35 0.2880699 0.515 0.248 9.160740e-31
## ESYT2 1.970157e-27 0.2880656 0.697 0.426 3.955681e-23
## SORL1 8.284846e-41 0.2878485 0.422 0.156 1.663431e-36
## TRPC4AP 1.526434e-34 0.2876847 0.534 0.276 3.064774e-30
## FAM133B 1.418459e-32 0.2874564 0.583 0.320 2.847983e-28
## TGFA 1.151145e-38 0.2873259 0.450 0.185 2.311269e-34
## COMMD10 1.139120e-31 0.2873046 0.597 0.325 2.287126e-27
## CYP4X1 2.658613e-34 0.2867921 0.480 0.225 5.337963e-30
## UBAC2 2.854729e-32 0.2866058 0.645 0.362 5.731725e-28
## DENND2C 2.802119e-20 0.2863037 0.624 0.425 5.626096e-16
## SMG6 1.212794e-30 0.2862509 0.631 0.360 2.435048e-26
## DTWD2 8.817360e-45 0.2861755 0.399 0.128 1.770350e-40
## ZNF407 4.288646e-32 0.2858762 0.595 0.317 8.610744e-28
## ADGRF1 4.811389e-32 0.2855002 0.825 0.637 9.660306e-28
## INVS 2.712915e-32 0.2853896 0.514 0.257 5.446990e-28
## FARP1 1.530645e-28 0.2853709 0.687 0.410 3.073229e-24
## ETV3 3.999755e-36 0.2852101 0.441 0.188 8.030708e-32
## NFIA 5.397160e-26 0.2844404 0.757 0.491 1.083642e-21
## TRIM44 1.856323e-30 0.2838418 0.618 0.354 3.727125e-26
## MBOAT2 1.053891e-29 0.2836650 0.595 0.332 2.116002e-25
## ARHGAP42 1.554315e-28 0.2832343 0.585 0.327 3.120753e-24
## SIPA1L2 9.194244e-32 0.2831040 0.544 0.284 1.846020e-27
## VPS13A 6.441814e-35 0.2828927 0.550 0.275 1.293387e-30
## CCDC91 4.433573e-30 0.2827793 0.634 0.361 8.901729e-26
## RHOBTB3 1.497018e-35 0.2826138 0.451 0.194 3.005713e-31
## DPH6 1.610816e-29 0.2824771 0.514 0.264 3.234196e-25
## ARHGAP12 6.983030e-36 0.2823936 0.520 0.246 1.402053e-31
## TBC1D22A 4.682802e-29 0.2821884 0.670 0.388 9.402130e-25
## PLXDC2 2.712984e-29 0.2819712 0.534 0.276 5.447129e-25
## FUT2 1.546846e-37 0.2819175 0.487 0.219 3.105758e-33
## PRKCA 1.669702e-31 0.2817059 0.502 0.242 3.352428e-27
## SLC31A1 2.209125e-39 0.2813915 0.446 0.181 4.435482e-35
## KLHL24 4.618065e-36 0.2812283 0.444 0.192 9.272150e-32
## ELP4 1.907469e-32 0.2811697 0.514 0.259 3.829815e-28
## FBXW11 8.401505e-31 0.2810837 0.595 0.335 1.686854e-26
## APP 1.055610e-25 0.2810077 0.724 0.481 2.119453e-21
## ESRP1 5.131322e-28 0.2809644 0.627 0.380 1.030267e-23
## TRAPPC9 2.629905e-28 0.2809359 0.766 0.430 5.280324e-24
## HIVEP2 5.205995e-34 0.2809250 0.450 0.207 1.045260e-29
## GNA14 7.553048e-28 0.2803688 0.654 0.352 1.516501e-23
## SLC16A7 2.643254e-44 0.2802330 0.384 0.115 5.307126e-40
## FAM20A 6.869306e-41 0.2801661 0.291 0.065 1.379219e-36
## C3 1.016257e-55 0.2800378 0.938 0.871 2.040440e-51
## MED13 3.532986e-31 0.2799340 0.584 0.322 7.093529e-27
## CELSR1 6.849339e-29 0.2798980 0.647 0.368 1.375210e-24
## RALGAPA1 1.657144e-32 0.2798558 0.601 0.316 3.327213e-28
## TPTEP2-CSNK1E 1.690810e-35 0.2797606 0.502 0.236 3.394808e-31
## YAP1 8.851644e-29 0.2793003 0.698 0.413 1.777233e-24
## TRMT44 1.420149e-28 0.2786624 0.502 0.262 2.851375e-24
## EPHA2 1.100138e-32 0.2786160 0.529 0.267 2.208857e-28
## HES4 4.305849e-23 0.2783216 0.687 0.487 8.645283e-19
## SMG1 1.484177e-33 0.2781981 0.578 0.302 2.979931e-29
## AP1G1 4.570436e-31 0.2779109 0.545 0.298 9.176522e-27
## TENT2 8.547827e-35 0.2779002 0.508 0.245 1.716233e-30
## HOMER1 2.103081e-28 0.2777245 0.375 0.164 4.222566e-24
## RERG 5.049370e-37 0.2777099 0.273 0.064 1.013812e-32
## UBTD2 2.099292e-34 0.2776241 0.451 0.202 4.214958e-30
## FAM83D 8.457245e-33 0.2772440 0.320 0.106 1.698046e-28
## STX5 8.078710e-37 0.2769518 0.475 0.213 1.622043e-32
## ABCC1 7.496689e-29 0.2769015 0.566 0.312 1.505185e-24
## MIA2 9.862355e-30 0.2768963 0.626 0.359 1.980164e-25
## GLI2 1.915505e-40 0.2764854 0.262 0.051 3.845951e-36
## ANXA11 2.386353e-32 0.2757017 0.870 0.687 4.791319e-28
## ZNF91 1.227725e-34 0.2756703 0.509 0.244 2.465027e-30
## CUL3 9.093303e-30 0.2754195 0.624 0.363 1.825753e-25
## TGM2 6.019837e-26 0.2751327 0.581 0.352 1.208663e-21
## GAK 1.908458e-31 0.2750039 0.573 0.311 3.831802e-27
## RFX7 1.555102e-30 0.2749694 0.559 0.297 3.122334e-26
## TMEM87A 4.945416e-31 0.2747604 0.589 0.319 9.929405e-27
## ACAP2 2.682026e-31 0.2747529 0.569 0.300 5.384971e-27
## ATP9A 3.570246e-29 0.2746272 0.598 0.332 7.168340e-25
## ARFGEF2 3.514481e-34 0.2742509 0.514 0.254 7.056374e-30
## WARS1 7.123908e-12 0.2740909 0.509 0.389 1.430338e-07
## NCOR1 2.962911e-27 0.2739659 0.673 0.410 5.948932e-23
## AGO3 4.133192e-33 0.2738039 0.532 0.268 8.298623e-29
## MAGI2 6.957894e-31 0.2737246 0.405 0.175 1.397006e-26
## R3HDM1 6.454640e-30 0.2736570 0.528 0.280 1.295963e-25
## NEO1 1.114588e-32 0.2735904 0.500 0.246 2.237870e-28
## SCEL 2.621311e-30 0.2734587 0.319 0.114 5.263067e-26
## MAST2 5.550792e-33 0.2734407 0.431 0.191 1.114488e-28
## CDK13 6.577747e-30 0.2732625 0.656 0.373 1.320680e-25
## THSD4 1.343207e-29 0.2730688 0.454 0.216 2.696891e-25
## DSP 1.361801e-24 0.2729979 0.735 0.522 2.734224e-20
## IL4R 1.050070e-35 0.2728401 0.467 0.204 2.108330e-31
## SUPT3H 3.224228e-30 0.2720483 0.429 0.201 6.473605e-26
## STS 5.313253e-23 0.2719826 0.582 0.338 1.066795e-18
## CDC42SE2 8.504235e-27 0.2719792 0.638 0.388 1.707480e-22
## ELF1 2.476645e-25 0.2716808 0.652 0.395 4.972608e-21
## IRAG2 1.010995e-21 0.2711361 0.607 0.401 2.029876e-17
## SLC20A2 5.219319e-25 0.2709617 0.652 0.391 1.047935e-20
## PRRC2C 1.050171e-26 0.2706714 0.661 0.418 2.108534e-22
## XBP1 7.138154e-22 0.2704597 0.640 0.468 1.433199e-17
## TRERF1 8.181382e-29 0.2703508 0.502 0.260 1.642658e-24
## SIL1 3.042838e-29 0.2702998 0.589 0.335 6.109411e-25
## TCN1 2.427178e-22 0.2698426 0.615 0.400 4.873288e-18
## ZNF721 2.912590e-27 0.2697791 0.588 0.344 5.847898e-23
## SGMS2 3.953419e-25 0.2697719 0.614 0.372 7.937674e-21
## ZC3H12C 2.414433e-34 0.2697633 0.389 0.156 4.847698e-30
## CXCL17 8.014884e-30 0.2696768 0.853 0.748 1.609228e-25
## TFDP2 6.122459e-26 0.2696535 0.618 0.376 1.229267e-21
## RIPK1 1.387966e-33 0.2694752 0.453 0.207 2.786759e-29
## CEACAM6 1.024944e-22 0.2693407 0.808 0.689 2.057882e-18
## CERT1 2.937300e-29 0.2689762 0.579 0.319 5.897512e-25
## TRIM22 5.800602e-26 0.2687879 0.581 0.345 1.164645e-21
## KIF26B 1.011649e-24 0.2681650 0.348 0.156 2.031189e-20
## BCKDHB 1.543724e-32 0.2681645 0.451 0.206 3.099489e-28
## PLEKHA6 1.408550e-33 0.2677472 0.506 0.241 2.828086e-29
## PLEKHG7 1.059954e-22 0.2675296 0.696 0.392 2.128176e-18
## PHF20L1 1.857557e-31 0.2673734 0.517 0.269 3.729603e-27
## WWOX 1.655690e-21 0.2672100 0.828 0.562 3.324294e-17
## DMXL1 2.781026e-36 0.2670915 0.478 0.212 5.583744e-32
## POLR2J3.1 1.952374e-33 0.2669904 0.549 0.271 3.919977e-29
## PPP2R2A 7.795875e-25 0.2666244 0.695 0.458 1.565256e-20
## KDM5B 5.239824e-27 0.2665594 0.588 0.335 1.052052e-22
## MYCBP2 3.960723e-25 0.2662299 0.670 0.421 7.952339e-21
## HMBOX1 4.931423e-33 0.2661842 0.497 0.242 9.901311e-29
## FOSB 2.365518e-23 0.2661009 0.662 0.447 4.749488e-19
## NPEPPS 3.901593e-28 0.2660785 0.606 0.343 7.833619e-24
## ELF3 7.530157e-44 0.2659280 0.922 0.818 1.511905e-39
## BMAL2 1.397156e-22 0.2653225 0.509 0.299 2.805211e-18
## STX8 1.885762e-25 0.2652538 0.661 0.423 3.786234e-21
## SKIL 3.070330e-28 0.2652353 0.505 0.266 6.164609e-24
## REEP3 1.017584e-25 0.2647014 0.609 0.374 2.043106e-21
## ATF7IP 6.488238e-28 0.2646836 0.588 0.329 1.302708e-23
## SON 5.799188e-26 0.2645223 0.750 0.532 1.164361e-21
## ARHGAP35 3.299597e-34 0.2645112 0.467 0.216 6.624930e-30
## MAL2 1.101314e-22 0.2644769 0.670 0.454 2.211219e-18
## FBXO11 2.775805e-26 0.2644179 0.627 0.377 5.573262e-22
## LRCH3 2.202165e-31 0.2642910 0.517 0.266 4.421507e-27
## AMBRA1 1.380887e-28 0.2641032 0.646 0.375 2.772544e-24
## DNAJC3 5.886739e-28 0.2639975 0.583 0.342 1.181939e-23
## RAB27B 1.110902e-27 0.2636857 0.479 0.251 2.230470e-23
## OFD1 4.126614e-31 0.2635800 0.525 0.266 8.285415e-27
## PDZD2 1.826015e-29 0.2632929 0.409 0.186 3.666273e-25
## GPBP1 5.383928e-25 0.2632345 0.675 0.420 1.080985e-20
## RAB1A 4.920451e-27 0.2631620 0.609 0.365 9.879282e-23
## SP140L 2.251318e-31 0.2631585 0.441 0.206 4.520196e-27
## ARHGEF12 5.505002e-27 0.2631433 0.590 0.340 1.105294e-22
## PELI2 2.674368e-32 0.2628743 0.368 0.143 5.369597e-28
## PLSCR1 3.769828e-25 0.2627887 0.770 0.567 7.569060e-21
## ATE1 2.593634e-33 0.2627632 0.549 0.275 5.207499e-29
## ACBD6 5.792553e-28 0.2625413 0.606 0.348 1.163029e-23
## GNPTAB 2.111838e-35 0.2625335 0.455 0.199 4.240149e-31
## ADD3 1.890529e-23 0.2623073 0.703 0.458 3.795804e-19
## PCLO 2.552834e-31 0.2620806 0.389 0.163 5.125581e-27
## PMM2 2.260954e-34 0.2619978 0.473 0.215 4.539543e-30
## NSD1 1.956951e-30 0.2619233 0.562 0.299 3.929166e-26
## PHF21A 6.588321e-29 0.2618891 0.525 0.275 1.322803e-24
## PRIM2 3.888635e-28 0.2617542 0.480 0.248 7.807602e-24
## CORO1C 1.506007e-32 0.2614130 0.437 0.194 3.023761e-28
## PHKB 2.828033e-28 0.2613914 0.578 0.333 5.678125e-24
## MYO5C 1.387050e-27 0.2612115 0.638 0.363 2.784919e-23
## ABHD17C 8.042087e-31 0.2611554 0.393 0.169 1.614690e-26
## PARP4 1.314397e-24 0.2607657 0.662 0.418 2.639047e-20
## ADAM17 2.947137e-31 0.2607171 0.528 0.266 5.917262e-27
## ICAM1 5.176330e-26 0.2605860 0.394 0.182 1.039304e-21
## SYBU 5.575220e-33 0.2604842 0.334 0.118 1.119393e-28
## NIBAN1 2.774587e-28 0.2604835 0.411 0.188 5.570816e-24
## EHF 2.870728e-25 0.2602980 0.819 0.621 5.763849e-21
## EHBP1 1.309696e-28 0.2602406 0.580 0.309 2.629607e-24
## CRACR2A 5.444902e-39 0.2600860 0.341 0.103 1.093227e-34
## VPS9D1 5.724299e-37 0.2600499 0.355 0.122 1.149325e-32
## CDR2 4.081933e-32 0.2599123 0.504 0.241 8.195706e-28
## DOP1A 2.202632e-28 0.2598469 0.521 0.279 4.422444e-24
## CNOT4 1.164607e-33 0.2594143 0.541 0.266 2.338299e-29
## HECTD1 3.418376e-24 0.2587518 0.730 0.471 6.863416e-20
## RNF38 5.887489e-28 0.2586443 0.527 0.283 1.182090e-23
## PPP6R2 1.452373e-28 0.2585497 0.551 0.303 2.916075e-24
## GPATCH8 3.544044e-28 0.2583280 0.575 0.317 7.115732e-24
## IGF2R 1.506323e-34 0.2582796 0.467 0.210 3.024396e-30
## FAM193A 8.075852e-31 0.2581287 0.491 0.247 1.621470e-26
## RUFY3 4.960181e-30 0.2581010 0.548 0.288 9.959051e-26
## ICA1 2.612731e-31 0.2580764 0.453 0.210 5.245842e-27
## KIDINS220 1.746910e-35 0.2579750 0.448 0.193 3.507446e-31
## UST 4.648794e-28 0.2578167 0.458 0.225 9.333848e-24
## SLC9A8 1.280686e-28 0.2577140 0.612 0.333 2.571361e-24
## CAMKMT 2.129186e-24 0.2571597 0.652 0.378 4.274980e-20
## RYK 4.775553e-32 0.2570191 0.448 0.213 9.588354e-28
## XIAP 7.687515e-30 0.2568127 0.526 0.271 1.543499e-25
## B3GNT6 2.530395e-45 0.2567148 0.283 0.047 5.080528e-41
## GBP2 7.328998e-28 0.2564446 0.479 0.244 1.471516e-23
## CCND3 1.150817e-23 0.2564263 0.605 0.371 2.310610e-19
## MPP7 9.661055e-28 0.2563899 0.530 0.289 1.939747e-23
## HEG1 4.288443e-24 0.2561294 0.306 0.128 8.610336e-20
## YTHDC1 1.935600e-29 0.2556216 0.567 0.308 3.886298e-25
## GIGYF2 5.580338e-30 0.2553237 0.525 0.276 1.120420e-25
## RBM39 2.192012e-23 0.2552484 0.748 0.523 4.401122e-19
## NCKAP1 4.027430e-27 0.2550878 0.560 0.324 8.086274e-23
## RRAS2 7.555206e-26 0.2547771 0.470 0.256 1.516934e-21
## KMT5B 6.265297e-30 0.2547324 0.569 0.301 1.257946e-25
## PUM2 1.617439e-25 0.2546770 0.694 0.411 3.247494e-21
## ELOVL5 1.083920e-23 0.2546746 0.482 0.266 2.176295e-19
## VRK2 7.459627e-25 0.2544679 0.590 0.347 1.497744e-20
## ENSG00000268797 3.730236e-40 0.2544604 0.360 0.111 7.489569e-36
## RBMS2 2.449338e-26 0.2544460 0.448 0.230 4.917781e-22
## DIP2C 8.785857e-31 0.2543395 0.416 0.180 1.764024e-26
## UBR2 6.306367e-29 0.2543346 0.552 0.295 1.266192e-24
## POGZ 1.370318e-29 0.2542478 0.475 0.239 2.751323e-25
## HERC2 3.746636e-31 0.2540191 0.509 0.252 7.522495e-27
## NIPAL3 4.756016e-34 0.2538335 0.450 0.200 9.549129e-30
## IFI44L 5.678249e-20 0.2537110 0.667 0.480 1.140079e-15
## PIK3C2A 8.718616e-30 0.2536478 0.525 0.275 1.750524e-25
## FLNB 7.285959e-27 0.2536350 0.571 0.315 1.462875e-22
## LONP2 1.302852e-30 0.2531666 0.542 0.288 2.615866e-26
## RNF207 9.181719e-44 0.2530894 0.357 0.104 1.843506e-39
## CAPN5 1.115812e-36 0.2527811 0.424 0.167 2.240327e-32
## PRELID2 1.415298e-28 0.2524569 0.309 0.117 2.841636e-24
## KDM5A 1.202346e-26 0.2524543 0.523 0.295 2.414070e-22
## VEZT 2.100965e-29 0.2522102 0.491 0.246 4.218317e-25
## MAP4K5 4.571045e-32 0.2522058 0.460 0.218 9.177744e-28
## GFOD1 8.163817e-30 0.2521422 0.394 0.171 1.639131e-25
## CLASP2 6.666625e-29 0.2518140 0.580 0.311 1.338525e-24
## SLC12A6 3.826346e-30 0.2516387 0.419 0.190 7.682538e-26
## PLEKHA2 9.441155e-29 0.2515910 0.388 0.177 1.895595e-24
## CREB3L2 5.340234e-35 0.2514350 0.385 0.146 1.072212e-30
## TMPRSS11D 7.234382e-32 0.2512167 0.394 0.159 1.452519e-27
## MLPH 1.421376e-39 0.2506550 0.386 0.128 2.853839e-35
## SPDYA 2.011137e-29 0.2506191 0.334 0.128 4.037960e-25
## PPP1R12B 2.350357e-23 0.2504970 0.645 0.385 4.719047e-19
## PNISR 8.254000e-25 0.2500768 0.583 0.346 1.657238e-20#write.csv(cal_infected_sec_vs_infected_cil_markers, "final/results/cal_infected_sec_vs_infected_cil_markers.csv")#Filter by ISGs
cal_infected_sec_vs_infected_cil_isgs <- cal_infected_sec_vs_infected_cil_markers %>%
filter(row.names(cal_infected_sec_vs_infected_cil_markers) %in% c(human.isgs$isg_name)) %>%
mutate(gene = rownames(.),
comparison = "secretory_vs_ciliated")
cal_infected_sec_vs_infected_cil_isgs## p_val avg_log2FC pct.1 pct.2 p_val_adj gene
## ARHGEF3 4.797006e-129 0.6967845 0.836 0.371 9.631428e-125 ARHGEF3
## ETV6 1.664708e-120 0.6194987 0.923 0.603 3.342401e-116 ETV6
## TCF7L2 3.706915e-101 0.6130464 0.815 0.380 7.442744e-97 TCF7L2
## DUSP5 1.236791e-108 0.6099508 0.813 0.426 2.483229e-104 DUSP5
## NAMPT 2.464187e-97 0.6023340 0.826 0.511 4.947596e-93 NAMPT
## B4GALT5 2.310022e-104 0.6008117 0.830 0.481 4.638062e-100 B4GALT5
## VMP1 2.676927e-104 0.5585072 0.854 0.591 5.374734e-100 VMP1
## FNDC3B 9.764366e-87 0.5433548 0.860 0.526 1.960489e-82 FNDC3B
## MAP3K5 8.865464e-80 0.5407392 0.736 0.361 1.780008e-75 MAP3K5
## STAT3 8.989100e-68 0.4852114 0.757 0.411 1.804831e-63 STAT3
## EXT1 2.384967e-64 0.4736187 0.880 0.550 4.788538e-60 EXT1
## SLC26A4 4.249497e-38 0.4702646 0.606 0.348 8.532139e-34 SLC26A4
## MYOF 5.664207e-64 0.4565777 0.806 0.492 1.137260e-59 MYOF
## MAFF 2.293224e-60 0.4261447 0.647 0.295 4.604336e-56 MAFF
## EPAS1 3.283580e-50 0.4223312 0.766 0.512 6.592772e-46 EPAS1
## NCOA3 3.579600e-55 0.4217191 0.697 0.356 7.187122e-51 NCOA3
## GALNT2 3.077846e-52 0.3893638 0.594 0.273 6.179699e-48 GALNT2
## EHD4 3.584920e-43 0.3868267 0.644 0.351 7.197802e-39 EHD4
## RNF213 6.435192e-40 0.3819029 0.793 0.516 1.292058e-35 RNF213
## MX2 3.896435e-30 0.3753024 0.664 0.470 7.823263e-26 MX2
## MCL1 2.818341e-44 0.3576880 0.784 0.516 5.658665e-40 MCL1
## GCH1 3.385188e-44 0.3515024 0.451 0.176 6.796779e-40 GCH1
## PHACTR4 1.029561e-37 0.3430273 0.710 0.406 2.067153e-33 PHACTR4
## SP100 3.664178e-37 0.3354059 0.799 0.545 7.356937e-33 SP100
## TMEM62 1.444537e-44 0.3326102 0.463 0.182 2.900341e-40 TMEM62
## CYTH1 7.490202e-37 0.3169111 0.644 0.348 1.503883e-32 CYTH1
## SMAD3 4.167571e-31 0.3164063 0.653 0.387 8.367649e-27 SMAD3
## N4BP1 1.585815e-30 0.3020420 0.578 0.326 3.184000e-26 N4BP1
## TRIM38 1.390488e-32 0.3011002 0.570 0.309 2.791822e-28 TRIM38
## HSH2D 2.761643e-34 0.2965751 0.516 0.257 5.544827e-30 HSH2D
## RBMS1 2.045637e-32 0.2956528 0.627 0.353 4.107230e-28 RBMS1
## RNF24 1.400828e-38 0.2942892 0.426 0.170 2.812582e-34 RNF24
## IDO1 3.425650e-18 0.2922384 0.574 0.399 6.878020e-14 IDO1
## MORC3 2.811177e-32 0.2921938 0.614 0.339 5.644282e-28 MORC3
## HES4 4.305849e-23 0.2783216 0.687 0.487 8.645283e-19 HES4
## GAK 1.908458e-31 0.2750039 0.573 0.311 3.831802e-27 GAK
## WARS1 7.123908e-12 0.2740909 0.509 0.389 1.430338e-07 WARS1
## ELF1 2.476645e-25 0.2716808 0.652 0.395 4.972608e-21 ELF1
## TRIM22 5.800602e-26 0.2687879 0.581 0.345 1.164645e-21 TRIM22
## PLSCR1 3.769828e-25 0.2627887 0.770 0.567 7.569060e-21 PLSCR1
## PMM2 2.260954e-34 0.2619978 0.473 0.215 4.539543e-30 PMM2
## ICAM1 5.176330e-26 0.2605860 0.394 0.182 1.039304e-21 ICAM1
## GBP2 7.328998e-28 0.2564446 0.479 0.244 1.471516e-23 GBP2
## CCND3 1.150817e-23 0.2564263 0.605 0.371 2.310610e-19 CCND3
## HEG1 4.288443e-24 0.2561294 0.306 0.128 8.610336e-20 HEG1
## IFI44L 5.678249e-20 0.2537110 0.667 0.480 1.140079e-15 IFI44L
## comparison
## ARHGEF3 secretory_vs_ciliated
## ETV6 secretory_vs_ciliated
## TCF7L2 secretory_vs_ciliated
## DUSP5 secretory_vs_ciliated
## NAMPT secretory_vs_ciliated
## B4GALT5 secretory_vs_ciliated
## VMP1 secretory_vs_ciliated
## FNDC3B secretory_vs_ciliated
## MAP3K5 secretory_vs_ciliated
## STAT3 secretory_vs_ciliated
## EXT1 secretory_vs_ciliated
## SLC26A4 secretory_vs_ciliated
## MYOF secretory_vs_ciliated
## MAFF secretory_vs_ciliated
## EPAS1 secretory_vs_ciliated
## NCOA3 secretory_vs_ciliated
## GALNT2 secretory_vs_ciliated
## EHD4 secretory_vs_ciliated
## RNF213 secretory_vs_ciliated
## MX2 secretory_vs_ciliated
## MCL1 secretory_vs_ciliated
## GCH1 secretory_vs_ciliated
## PHACTR4 secretory_vs_ciliated
## SP100 secretory_vs_ciliated
## TMEM62 secretory_vs_ciliated
## CYTH1 secretory_vs_ciliated
## SMAD3 secretory_vs_ciliated
## N4BP1 secretory_vs_ciliated
## TRIM38 secretory_vs_ciliated
## HSH2D secretory_vs_ciliated
## RBMS1 secretory_vs_ciliated
## RNF24 secretory_vs_ciliated
## IDO1 secretory_vs_ciliated
## MORC3 secretory_vs_ciliated
## HES4 secretory_vs_ciliated
## GAK secretory_vs_ciliated
## WARS1 secretory_vs_ciliated
## ELF1 secretory_vs_ciliated
## TRIM22 secretory_vs_ciliated
## PLSCR1 secretory_vs_ciliated
## PMM2 secretory_vs_ciliated
## ICAM1 secretory_vs_ciliated
## GBP2 secretory_vs_ciliated
## CCND3 secretory_vs_ciliated
## HEG1 secretory_vs_ciliated
## IFI44L secretory_vs_ciliated#write.csv(cal_infected_sec_vs_infected_cil_isgs, "final/results/cal_infected_sec_vs_infected_cil_isgs.csv")I tried to make a combined bubble plot of infected sec/goblet vs cil and infected cil vs sec/goblet:
#pdf("final/results/Comparison of infected ciliated vs secretory ISG bubble plot.pdf")
rbind(cal_infected_sec_vs_infected_cil_isgs, cal_infected_cil_vs_infected_sec_isgs) %>%
group_by(comparison) %>%
mutate(gene = fct_reorder(gene, avg_log2FC)) %>%
ggplot(aes(x = avg_log2FC, y = gene, fill = comparison)) +
geom_dotplot(binaxis = "y", binwidth = 1, position = "identity") +
labs(x = "Average log2 fold change",
y = "Gene ID") +
ggtitle("ISGs upregulated in secretory/goblet vs Ciliated cells or vice versa\nCal infection condition") +
theme_bw()
#dev.off()Heatmap of ISG expression in mock condition
#Set identities of integrated object to the condition
Idents(seurat_integrated) <- "condition"
#Pull out mock dataset
mock_integrated <- subset(seurat_integrated, idents = c("mock"))
#Scale data in the RNA assays
mock_integrated <- ScaleData(mock_integrated, features = present_isgs$isg_name)## Centering and scaling data matrix#Set identities to cell type
Idents(mock_integrated) <- "customclassif"
#Find markers for each cell type
mock_markers <- FindAllMarkers(mock_integrated)## Calculating cluster Ciliated cells## Calculating cluster Secretory and goblet cells## Calculating cluster Basal cells## Calculating cluster Airway epithelial cells## Calculating cluster Club cells## Calculating cluster Ionocytes#Filter markers down to ISGs, and filter to only padj < 0.05
mock_isg_markers_all <- mock_markers %>%
filter(rownames(.) %in% present_isgs$isg_name & p_val_adj < 0.05) %>%
group_by(cluster) %>%
arrange(-avg_log2FC, .by_group = TRUE)
#Top 50 only (use for graphing)
mock_isg_markers <- mock_markers %>%
filter(rownames(.) %in% present_isgs$isg_name & p_val_adj < 0.05) %>%
group_by(cluster) %>%
slice_max(n = 50, order_by = avg_log2FC) %>%
arrange(-avg_log2FC, .by_group = TRUE)
#Heatmap of mock condition, cell type specific ISGs
#pdf("final/results/mock_isg_heatmap.pdf")
DoHeatmap(mock_integrated, features = mock_isg_markers$gene, assay = "RNA", group.colors = c("#1080FF", "#8000FF", "#FB02FF", "#00C13B", "#48EFDA", "#CC9ECB")) &
ggtitle("ISG Heatmap of Mock-infected cells\nCombined analysis") &
labs(caption = "Cell type identities were determined with full gene set.\nThe most variable genes per cluster were found with FindAllMarkers.\nCell type specific markers were filtered to ISGs and padj < 0.05.\n50 ISGs per cluster are plotted (or as many as are there if less than 50).")
#dev.off()Heatmap of proviral host factors across cell types
I used a list of 101 host factors for influenza identified in Tripathi et al., 2015 (PMID 26651948) and created a heatmap of how these markers were varying in the different cell types in the mock condition.
The paper identified common host factors identified across multiple
siRNA screens for IAV. I used only host factors that were confirmed in
multiple studies. The list is saved at
iav_host_factors_tripathi.csv.
#Find markers between uninfected and infected cell types
host_factors <- read.csv("iav_host_factors_tripathi.csv", header = TRUE)
#pdf("final/results/IAV host factor heatmap- mock condition.pdf", height = 20, width = 15)
DoHeatmap(mock_integrated, features = host_factors$host_factor, assay = "RNA", slot = "data", group.colors = c("#1080FF", "#8000FF", "#FB02FF", "#00C13B", "#48EFDA", "#CC9ECB")) &
ggtitle("IAV host factor heatmap\nMock condition only") &
theme(axis.text = element_text(size = 10))## Warning in DoHeatmap(mock_integrated, features = host_factors$host_factor, :
## The following features were omitted as they were not found in the data slot for
## the RNA assay: C17ORF85, NUPL1, BZRAP1, NHP2L1
#dev.off()Seems like most host factors are not expressed highly at baseline, except for some in the middle.
If I make a heatmap of proviral genes but use the features in the
Seurat object that are highly variable (those that are in the
scale.data slot of the RNA assay), only 6 genes are
available to use. This means the majority of these host factors are not
variable from cell to cell:
#pdf("final/results/IAV host factor heatmap- mock condition variable features only.pdf")
DoHeatmap(mock_integrated, features = host_factors$host_factor, assay = "RNA", group.colors = c("#1080FF", "#8000FF", "#FB02FF", "#00C13B", "#48EFDA", "#CC9ECB")) &
ggtitle("IAV host factor heatmap\nMock condition only\nVariable features only") &
theme(axis.text = element_text(size = 10))## Warning in DoHeatmap(mock_integrated, features = host_factors$host_factor, :
## The following features were omitted as they were not found in the scale.data
## slot for the RNA assay: TUBB, DCLK2, WDR18, PSENEN, C17ORF85, PPP1R14D,
## RACGAP1, CLIC4, IL17RA, DAPK2, SF3B1, SF3B3, CLUH, TBC1D1, NUP205, SNW1, COPG1,
## STK38, SF3B2, PLK4, POLD3, IVNS1ABP, PRPF8, NXF1, SF3A1, SIGMAR1, CALCOCO2,
## PSMD14, ATP6AP2, TRIM28, MED6, NUPL1, EIF4A3, TTI1, CLOCK, EFTUD2, COPB2,
## BZRAP1, BUB3, OSMR, ATP6V0D1, CFLAR, RIOK3, EIF3A, NUP214, XPO1, TTN, SUPT6H,
## SNRPD3, SNRPB, SNRPA1, SNRNP70, SLC1A3, SELPLG, RPS16, RPS14, RPS10, RAB5A,
## PTPRN, PSMD2, PSMD1, PSMA1, MAP2K3, MAPK13, PRKACA, PHF2, PGD, NUP98, NHP2L1,
## MPG, MFAP1, MDM2, LGALS3BP, KPNB1, JUN, ITGA3, HSPD1, HRAS, FUS, FGFR2, BPTF,
## EGFR, DHX8, COPB1, COPA, PLK3, CLK1, CD81, CAD, ATP6AP1, ATP6V0B, ATP6V0C,
## ATP6V1B2, ATP6V1A, ARCN1
#dev.off()sessionInfo()## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /common/software/install/migrated/openblas/0.3.5_gcc8.2.0_multiarch/lib/libopenblasp-r0.3.5.so; LAPACK version 3.8.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: US/Central
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_1.0.0 purrr_1.0.2 data.table_1.15.0 stringr_1.5.0
## [5] openxlsx_4.2.5.2 HGNChelper_0.8.1 dplyr_1.1.4 ggplot2_3.4.4
## [9] sctransform_0.4.1 SeuratObject_5.0.1 Seurat_4.3.0
##
## loaded via a namespace (and not attached):
## [1] RcppAnnoy_0.0.22 splines_4.3.0
## [3] later_1.3.1 bitops_1.0-7
## [5] tibble_3.2.1 polyclip_1.10-6
## [7] lifecycle_1.0.4 Rdpack_2.6
## [9] globals_0.16.2 lattice_0.21-8
## [11] MASS_7.3-58.4 magrittr_2.0.3
## [13] limma_3.58.1 plotly_4.10.4
## [15] sass_0.4.7 rmarkdown_2.25
## [17] plotrix_3.8-4 jquerylib_0.1.4
## [19] yaml_2.3.7 qqconf_1.3.2
## [21] httpuv_1.6.14 sn_2.1.1
## [23] glmGamPoi_1.14.0 spam_2.10-0
## [25] zip_2.3.0 sp_2.1-3
## [27] spatstat.sparse_3.0-3 reticulate_1.34.0
## [29] cowplot_1.1.3 pbapply_1.7-2
## [31] RColorBrewer_1.1-3 multcomp_1.4-25
## [33] abind_1.4-5 zlibbioc_1.48.0
## [35] Rtsne_0.17 GenomicRanges_1.54.1
## [37] BiocGenerics_0.48.1 RCurl_1.98-1.14
## [39] TH.data_1.1-2 sandwich_3.1-0
## [41] GenomeInfoDbData_1.2.11 IRanges_2.36.0
## [43] S4Vectors_0.40.2 ggrepel_0.9.5
## [45] irlba_2.3.5.1 listenv_0.9.1
## [47] spatstat.utils_3.0-4 TFisher_0.2.0
## [49] goftest_1.2-3 spatstat.random_3.2-2
## [51] fitdistrplus_1.1-11 parallelly_1.36.0
## [53] DelayedMatrixStats_1.24.0 leiden_0.4.3.1
## [55] codetools_0.2-19 DelayedArray_0.28.0
## [57] tidyselect_1.2.0 farver_2.1.1
## [59] matrixStats_1.2.0 stats4_4.3.0
## [61] spatstat.explore_3.2-5 mathjaxr_1.6-0
## [63] jsonlite_1.8.7 multtest_2.58.0
## [65] ellipsis_0.3.2 progressr_0.14.0
## [67] ggridges_0.5.6 survival_3.5-5
## [69] tools_4.3.0 ica_1.0-3
## [71] Rcpp_1.0.11 glue_1.6.2
## [73] mnormt_2.1.1 gridExtra_2.3
## [75] SparseArray_1.2.3 xfun_0.41
## [77] metap_1.9 mgcv_1.8-42
## [79] MatrixGenerics_1.14.0 GenomeInfoDb_1.38.5
## [81] withr_3.0.0 numDeriv_2016.8-1.1
## [83] fastmap_1.1.1 fansi_1.0.5
## [85] digest_0.6.33 R6_2.5.1
## [87] mime_0.12 colorspace_2.1-0
## [89] scattermore_1.2 tensor_1.5
## [91] spatstat.data_3.0-4 utf8_1.2.4
## [93] tidyr_1.3.1 generics_0.1.3
## [95] httr_1.4.7 htmlwidgets_1.6.2
## [97] S4Arrays_1.2.0 uwot_0.1.16
## [99] pkgconfig_2.0.3 gtable_0.3.4
## [101] lmtest_0.9-40 XVector_0.42.0
## [103] htmltools_0.5.7 dotCall64_1.1-1
## [105] scales_1.3.0 Biobase_2.62.0
## [107] png_0.1-8 knitr_1.45
## [109] rstudioapi_0.15.0 reshape2_1.4.4
## [111] nlme_3.1-162 cachem_1.0.8
## [113] zoo_1.8-12 KernSmooth_2.23-20
## [115] parallel_4.3.0 miniUI_0.1.1.1
## [117] pillar_1.9.0 grid_4.3.0
## [119] vctrs_0.6.5 RANN_2.6.1
## [121] promises_1.2.1 xtable_1.8-4
## [123] cluster_2.1.4 evaluate_0.23
## [125] mvtnorm_1.2-4 cli_3.6.1
## [127] compiler_4.3.0 rlang_1.1.3
## [129] crayon_1.5.2 future.apply_1.11.1
## [131] mutoss_0.1-13 labeling_0.4.3
## [133] plyr_1.8.9 stringi_1.8.3
## [135] viridisLite_0.4.2 deldir_2.0-2
## [137] munsell_0.5.0 lazyeval_0.2.2
## [139] spatstat.geom_3.2-8 Matrix_1.6-5
## [141] patchwork_1.2.0 sparseMatrixStats_1.14.0
## [143] future_1.33.1 statmod_1.5.0
## [145] shiny_1.8.0 SummarizedExperiment_1.32.0
## [147] highr_0.10 rbibutils_2.2.16
## [149] ROCR_1.0-11 igraph_1.5.1
## [151] bslib_0.5.1